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2023
Conference article
Open Access
Geolet: an interpretable model for trajectory classification
Landi C., Spinnato F., Guidotti R., Monreale A., Nanni M.
The large and diverse availability of mobility data enables the development of predictive models capable of recognizing various types of movements. Through a variety of GPS devices, any moving entity, animal, person, or vehicle can generate spatio-temporal trajectories. This data is used to infer migration patterns, manage traffic in large cities, and monitor the spread and impact of diseases, all critical situations that necessitate a thorough understanding of the underlying problem. Researchers, businesses, and governments use mobility data to make decisions that affect people's lives in many ways, employing accurate but opaque deep learning models that are difficult to interpret from a human standpoint. To address these limitations, we propose Geolet, a human-interpretable machine-learning model for trajectory classification. We use discriminative sub-trajectories extracted from mobility data to turn trajectories into a simplified representation that can be used as input by any machine learning classifier. We test our approach against state-of-the-art competitors on real-world datasets. Geolet outperforms black-box models in terms of accuracy while being orders of magnitude faster than its interpretable competitors.Source: IDA 2023 - 21st Symposium on Intelligent Data Analysis, pp. 236–248, Louvain-la-Neuve, Belgium, 12-14/04/2023
DOI: 10.1007/978-3-031-30047-9_19
Project(s): TAILOR
, XAI , SoBigData-PlusPlus , Humane AI
Metrics:
Landi C., Spinnato F., Guidotti R., Monreale A., Nanni M.
The large and diverse availability of mobility data enables the development of predictive models capable of recognizing various types of movements. Through a variety of GPS devices, any moving entity, animal, person, or vehicle can generate spatio-temporal trajectories. This data is used to infer migration patterns, manage traffic in large cities, and monitor the spread and impact of diseases, all critical situations that necessitate a thorough understanding of the underlying problem. Researchers, businesses, and governments use mobility data to make decisions that affect people's lives in many ways, employing accurate but opaque deep learning models that are difficult to interpret from a human standpoint. To address these limitations, we propose Geolet, a human-interpretable machine-learning model for trajectory classification. We use discriminative sub-trajectories extracted from mobility data to turn trajectories into a simplified representation that can be used as input by any machine learning classifier. We test our approach against state-of-the-art competitors on real-world datasets. Geolet outperforms black-box models in terms of accuracy while being orders of magnitude faster than its interpretable competitors.Source: IDA 2023 - 21st Symposium on Intelligent Data Analysis, pp. 236–248, Louvain-la-Neuve, Belgium, 12-14/04/2023
DOI: 10.1007/978-3-031-30047-9_19
Project(s): TAILOR
, XAI , SoBigData-PlusPlus , Humane AI Metrics:
See at:
ISTI Repository 
| doi.org 
| link.springer.com | CNR ExploRA
2023
Journal article
Open Access
Solving imbalanced learning with outlier detection and features reduction
Lusito S., Pugnana A., Guidotti R.
A critical problem for several real world applications is class imbalance. Indeed, in contexts like fraud detection or medical diagnostics, standard machine learning models fail because they are designed to handle balanced class distributions. Existing solutions typically increase the rare class instances by generating synthetic records to achieve a balanced class distribution. However, these procedures generate not plausible data and tend to create unnecessary noise. We propose a change of perspective where instead of relying on resampling techniques, we depend on unsupervised features engineering approaches to represent records with a combination of features that will help the classifier capturing the differences among classes, even in presence of imbalanced data. Thus, we combine a large array of outlier detection, features projection, and features selection approaches to augment the expressiveness of the dataset population. We show the effectiveness of our proposal in a deep and wide set of benchmarking experiments as well as in real case studies.Source: Machine learning (2023). doi:10.1007/s10994-023-06448-0
DOI: 10.1007/s10994-023-06448-0
Project(s): SoBigData-PlusPlus
Metrics:
Lusito S., Pugnana A., Guidotti R.
A critical problem for several real world applications is class imbalance. Indeed, in contexts like fraud detection or medical diagnostics, standard machine learning models fail because they are designed to handle balanced class distributions. Existing solutions typically increase the rare class instances by generating synthetic records to achieve a balanced class distribution. However, these procedures generate not plausible data and tend to create unnecessary noise. We propose a change of perspective where instead of relying on resampling techniques, we depend on unsupervised features engineering approaches to represent records with a combination of features that will help the classifier capturing the differences among classes, even in presence of imbalanced data. Thus, we combine a large array of outlier detection, features projection, and features selection approaches to augment the expressiveness of the dataset population. We show the effectiveness of our proposal in a deep and wide set of benchmarking experiments as well as in real case studies.Source: Machine learning (2023). doi:10.1007/s10994-023-06448-0
DOI: 10.1007/s10994-023-06448-0
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2023
Conference article
Restricted
Text to time series representations: towards interpretable predictive models
Poggioli M., Spinnato F., Guidotti R.
Time Series Analysis (TSA) and Natural Language Processing (NLP) are two domains of research that have seen a surge of interest in recent years. NLP focuses mainly on enabling computers to manipulate and generate human language, whereas TSA identifies patterns or components in time-dependent data. Given their different purposes, there has been limited exploration of combining them. In this study, we present an approach to convert text into time series to exploit TSA for exploring text properties and to make NLP approaches interpretable for humans. We formalize our Text to Time Series framework as a feature extraction and aggregation process, proposing a set of different conversion alternatives for each step. We experiment with our approach on several textual datasets, showing the conversion approach's performance and applying it to the field of interpretable time series classification.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 230–245, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_16
Metrics:
Poggioli M., Spinnato F., Guidotti R.
Time Series Analysis (TSA) and Natural Language Processing (NLP) are two domains of research that have seen a surge of interest in recent years. NLP focuses mainly on enabling computers to manipulate and generate human language, whereas TSA identifies patterns or components in time-dependent data. Given their different purposes, there has been limited exploration of combining them. In this study, we present an approach to convert text into time series to exploit TSA for exploring text properties and to make NLP approaches interpretable for humans. We formalize our Text to Time Series framework as a feature extraction and aggregation process, proposing a set of different conversion alternatives for each step. We experiment with our approach on several textual datasets, showing the conversion approach's performance and applying it to the field of interpretable time series classification.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 230–245, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_16
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2023
Conference article
Restricted
Interpretable data partitioning through tree-based clustering methods
Guidotti R., Landi C., Beretta A., Fadda D., Nanni M.
Interpretable Data Partitioning Through Tree-Based Clustering Methods Riccardo Guidotti, Cristiano Landi, Andrea Beretta, Daniele Fadda & Mirco Nanni Conference paper First Online: 08 October 2023 311 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14276) The growing interpretable machine learning research field is mainly focusing on the explanation of supervised approaches. However, also unsupervised approaches might benefit from considering interpretability aspects. While existing clustering methods only provide the assignment of records to clusters without justifying the partitioning, we propose tree-based clustering methods that offer interpretable data partitioning through a shallow decision tree. These decision trees enable easy-to-understand explanations of cluster assignments through short and understandable split conditions. The proposed methods are evaluated through experiments on synthetic and real datasets and proved to be more effective than traditional clustering approaches and interpretable ones in terms of standard evaluation measures and runtime. Finally, a case study involving human participation demonstrates the effectiveness of the interpretable clustering trees returned by the proposed method.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 492–507, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_33
Metrics:
Guidotti R., Landi C., Beretta A., Fadda D., Nanni M.
Interpretable Data Partitioning Through Tree-Based Clustering Methods Riccardo Guidotti, Cristiano Landi, Andrea Beretta, Daniele Fadda & Mirco Nanni Conference paper First Online: 08 October 2023 311 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14276) The growing interpretable machine learning research field is mainly focusing on the explanation of supervised approaches. However, also unsupervised approaches might benefit from considering interpretability aspects. While existing clustering methods only provide the assignment of records to clusters without justifying the partitioning, we propose tree-based clustering methods that offer interpretable data partitioning through a shallow decision tree. These decision trees enable easy-to-understand explanations of cluster assignments through short and understandable split conditions. The proposed methods are evaluated through experiments on synthetic and real datasets and proved to be more effective than traditional clustering approaches and interpretable ones in terms of standard evaluation measures and runtime. Finally, a case study involving human participation demonstrates the effectiveness of the interpretable clustering trees returned by the proposed method.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 492–507, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_33
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2023
Conference article
Restricted
Handling missing values in local post-hoc explainability
Cinquini M., Giannotti F., Guidotti R., Mattei A.
Missing data are quite common in real scenarios when using Artificial Intelligence (AI) systems for decision-making with tabular data and effectively handling them poses a significant challenge for such systems. While some machine learning models used by AI systems can tackle this problem, the existing literature lacks post-hoc explainability approaches able to deal with predictors that encounter missing data. In this paper, we extend a widely used local model-agnostic post-hoc explanation approach that enables explainability in the presence of missing values by incorporating state-of-the-art imputation methods within the explanation process. Since our proposal returns explanations in the form of feature importance, the user will be aware also of the importance of a missing value in a given record for a particular prediction. Extensive experiments show the effectiveness of the proposed method with respect to some baseline solutions relying on traditional data imputation.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 256–278, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_14
Project(s): TAILOR, XAI , SoBigData-PlusPlus , Humane AI
Metrics:
Cinquini M., Giannotti F., Guidotti R., Mattei A.
Missing data are quite common in real scenarios when using Artificial Intelligence (AI) systems for decision-making with tabular data and effectively handling them poses a significant challenge for such systems. While some machine learning models used by AI systems can tackle this problem, the existing literature lacks post-hoc explainability approaches able to deal with predictors that encounter missing data. In this paper, we extend a widely used local model-agnostic post-hoc explanation approach that enables explainability in the presence of missing values by incorporating state-of-the-art imputation methods within the explanation process. Since our proposal returns explanations in the form of feature importance, the user will be aware also of the importance of a missing value in a given record for a particular prediction. Extensive experiments show the effectiveness of the proposed method with respect to some baseline solutions relying on traditional data imputation.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 256–278, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_14
Project(s): TAILOR
, XAI , SoBigData-PlusPlus , Humane AI Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2023
Conference article
Restricted
Explaining black-boxes in federated learning
Corbucci L., Guidotti R., Monreale A.
Federated Learning has witnessed increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the work in the literature proposes algorithms and architectures for training neural networks, which although they present high performance in different predicting tasks and are easy to be learned with a cooperative mechanism, their predictive reasoning is obscure. Therefore, in this paper, we propose a variant of SHAP, one of the most widely used explanation methods, tailored to Horizontal server-based Federated Learning. The basic idea is having the possibility to explain an instance's prediction performed by the trained Machine Leaning model as an aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 151–163, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_8
Project(s): TAILOR, XAI , SoBigData-PlusPlus , Humane AI
Metrics:
Corbucci L., Guidotti R., Monreale A.
Federated Learning has witnessed increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the work in the literature proposes algorithms and architectures for training neural networks, which although they present high performance in different predicting tasks and are easy to be learned with a cooperative mechanism, their predictive reasoning is obscure. Therefore, in this paper, we propose a variant of SHAP, one of the most widely used explanation methods, tailored to Horizontal server-based Federated Learning. The basic idea is having the possibility to explain an instance's prediction performed by the trained Machine Leaning model as an aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 151–163, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_8
Project(s): TAILOR
, XAI , SoBigData-PlusPlus , Humane AI Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
City indicators for geographical transfer learning: an application to crash prediction
Nanni M., Guidotti R., Bonavita A., Alamdari O. I.
The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users' mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution.Source: Geoinformatica (Dordrecht) (2022). doi:10.1007/s10707-022-00464-3
DOI: 10.1007/s10707-022-00464-3
Project(s): Track and Know, Track and Know , SoBigData-PlusPlus
Metrics:
Nanni M., Guidotti R., Bonavita A., Alamdari O. I.
The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users' mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution.Source: Geoinformatica (Dordrecht) (2022). doi:10.1007/s10707-022-00464-3
DOI: 10.1007/s10707-022-00464-3
Project(s): Track and Know
, Track and Know , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | GeoInformatica | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
Explaining short text classification with diverse synthetic exemplars and counter-exemplars
Lampridis O., State L., Guidotti R., Ruggieri S.
We present xspells, a model-agnostic local approach for explaining the decisions of black box models in classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences - albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. Moreover, diversity of counter-exemplars is modeled as an optimization problem, solved by a greedy algorithm with theoretical guarantee. We report experiments on three datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, diversity, and usefulness, and that is comparable to it in terms of stability.Source: Machine learning (2022). doi:10.1007/s10994-022-06150-7
DOI: 10.1007/s10994-022-06150-7
Project(s): NoBIAS, SoBigData-PlusPlus
Metrics:
Lampridis O., State L., Guidotti R., Ruggieri S.
We present xspells, a model-agnostic local approach for explaining the decisions of black box models in classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences - albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. Moreover, diversity of counter-exemplars is modeled as an optimization problem, solved by a greedy algorithm with theoretical guarantee. We report experiments on three datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, diversity, and usefulness, and that is comparable to it in terms of stability.Source: Machine learning (2022). doi:10.1007/s10994-022-06150-7
DOI: 10.1007/s10994-022-06150-7
Project(s): NoBIAS
, SoBigData-PlusPlus Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Individual and collective stop-based adaptive trajectory segmentation
Bonavita A., Guidotti R., Nanni M.
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.Source: Geoinformatica (Dordrecht) (2021). doi:10.1007/s10707-021-00449-8
DOI: 10.1007/s10707-021-00449-8
Project(s): Track and Know
Metrics:
Bonavita A., Guidotti R., Nanni M.
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.Source: Geoinformatica (Dordrecht) (2021). doi:10.1007/s10707-021-00449-8
DOI: 10.1007/s10707-021-00449-8
Project(s): Track and Know
Metrics:
See at:
link.springer.com | GeoInformatica | GeoInformatica | ISTI Repository | ISTI Repository | CNR ExploRA
2020
Contribution to book
Open Access
Explaining multi-label black-box classifiers for health applications
Panigutti C., Guidotti R., Monreale A., Pedreschi D.
Today the state-of-the-art performance in classification is achieved by the so-called âEURoeblack boxesâEUR, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.Source: Precision Health and Medicine. A Digital Revolution in Healthcare, edited by Arash Shaban-Nejad, Martin Michalowski, pp. 97–110, 2020
DOI: 10.1007/978-3-030-24409-5_9
Metrics:
Panigutti C., Guidotti R., Monreale A., Pedreschi D.
Today the state-of-the-art performance in classification is achieved by the so-called âEURoeblack boxesâEUR, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.Source: Precision Health and Medicine. A Digital Revolution in Healthcare, edited by Arash Shaban-Nejad, Martin Michalowski, pp. 97–110, 2020
DOI: 10.1007/978-3-030-24409-5_9
Metrics:
See at:
media.springer.com | doi.org | link.springer.com | CNR ExploRA
2020
Conference article
Open Access
Black box explanation by learning image exemplars in the latent feature space
Guidotti R., Monreale A., Matwin S., Pedreschi D.
We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by "morphing" into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.Source: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, pp. 189–205, Wurzburg, Germany, 16-20 September, 2019
DOI: 10.1007/978-3-030-46150-8_12
DOI: 10.48550/arxiv.2002.03746
Project(s): AI4EU, Track and Know , Track and Know , PRO-RES , SoBigData
Metrics:
Guidotti R., Monreale A., Matwin S., Pedreschi D.
We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by "morphing" into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.Source: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, pp. 189–205, Wurzburg, Germany, 16-20 September, 2019
DOI: 10.1007/978-3-030-46150-8_12
DOI: 10.48550/arxiv.2002.03746
Project(s): AI4EU
, Track and Know , Track and Know , PRO-RES , SoBigData Metrics:
See at:
arXiv.org e-Print Archive | arxiv.org | ISTI Repository | doi.org | doi.org | www.springerprofessional.de | CNR ExploRA
2020
Conference article
Closed Access
Global explanations with local scoring
Setzu M., Guidotti R., Monreale A., Turini F.
Artificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these "black box" models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.Source: Joint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2019, pp. 159–171, Würzburg, Germany, 16-20 September, 2019
DOI: 10.1007/978-3-030-43823-4_14
Project(s): AI4EU, Track and Know , Track and Know , PRO-RES , XAI , SoBigData
Metrics:
Setzu M., Guidotti R., Monreale A., Turini F.
Artificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these "black box" models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.Source: Joint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2019, pp. 159–171, Würzburg, Germany, 16-20 September, 2019
DOI: 10.1007/978-3-030-43823-4_14
Project(s): AI4EU
, Track and Know , Track and Know , PRO-RES , XAI , SoBigData Metrics:
See at:
Communications in Computer and Information Science | link.springer.com | CNR ExploRA
2020
Conference article
Open Access
Self-Adapting Trajectory Segmentation
Bonavita A., Guidotti R., Nanni M.
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, 30/03/2020
Project(s): Track and Know
Bonavita A., Guidotti R., Nanni M.
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, 30/03/2020
Project(s): Track and Know
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2020
Conference article
Open Access
Data-Driven Location Annotation for Fleet Mobility Modeling
Guidotti R., Nanni M., Sbolgi F.
The large availability of mobility data allows studying human behavior and human activities. However, this massive and raw amount of data generally lacks any detailed semantics or useful categorization. Annotations of the locations where the users stop may be helpful in a number of contexts, including user modeling and profiling, urban planning, activity recommendations, and can even lead to a deeper understanding of the mobility evolution of an urban area. In this paper, we foster the expressive power of individual mobility networks, a data model describing users' behavior, by defining a data-driven procedure for locations annotation. The procedure considers individual, collective, and contextual features for turning locations into annotated ones. The annotated locations own a high expressiveness that allows generalizing individual mobility networks, and that makes them comparable across different users. The results of our study on a dataset of trucks moving in Greece show that the annotated individual mobility networks can enable detailed analysis of urban areas and the planning of advanced mobility applications.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, 30/03/2020
Project(s): Track and Know
Guidotti R., Nanni M., Sbolgi F.
The large availability of mobility data allows studying human behavior and human activities. However, this massive and raw amount of data generally lacks any detailed semantics or useful categorization. Annotations of the locations where the users stop may be helpful in a number of contexts, including user modeling and profiling, urban planning, activity recommendations, and can even lead to a deeper understanding of the mobility evolution of an urban area. In this paper, we foster the expressive power of individual mobility networks, a data model describing users' behavior, by defining a data-driven procedure for locations annotation. The procedure considers individual, collective, and contextual features for turning locations into annotated ones. The annotated locations own a high expressiveness that allows generalizing individual mobility networks, and that makes them comparable across different users. The results of our study on a dataset of trucks moving in Greece show that the annotated individual mobility networks can enable detailed analysis of urban areas and the planning of advanced mobility applications.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, 30/03/2020
Project(s): Track and Know
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
(So) Big Data and the transformation of the city
Andrienko G., Andrienko N., Boldrini C., Caldarelli G., Cintia P., Cresci S., Facchini A., Giannotti F., Gionis A., Guidotti R., Mathioudakis M., Muntean C. I., Pappalardo L., Pedreschi D., Pournaras E., Pratesi F., Tesconi M., Trasarti R.
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.Source: International Journal of Data Science and Analytics (Print) 1 (2020). doi:10.1007/s41060-020-00207-3
DOI: 10.1007/s41060-020-00207-3
Project(s): SoBigData
Metrics:
Andrienko G., Andrienko N., Boldrini C., Caldarelli G., Cintia P., Cresci S., Facchini A., Giannotti F., Gionis A., Guidotti R., Mathioudakis M., Muntean C. I., Pappalardo L., Pedreschi D., Pournaras E., Pratesi F., Tesconi M., Trasarti R.
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.Source: International Journal of Data Science and Analytics (Print) 1 (2020). doi:10.1007/s41060-020-00207-3
DOI: 10.1007/s41060-020-00207-3
Project(s): SoBigData
Metrics:
See at:
Aaltodoc Publication Archive | International Journal of Data Science and Analytics | White Rose Research Online | HELDA - Digital Repository of the University of Helsinki | Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari | link.springer.com | International Journal of Data Science and Analytics | City Research Online | ISTI Repository | Fraunhofer-ePrints | CNR ExploRA
2020
Journal article
Open Access
Human migration: the big data perspective
Sîrbu A., Andrienko G., Andrienko N., Boldrini C., Conti M., Giannotti F., Guidotti R., Bertoli S., Kim J., Muntean C. I., Pappalardo L., Passarella A., Pedreschi D., Pollacci L., Pratesi F., Sharma R.
How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.Source: International Journal of Data Science and Analytics (Online) 11 (2020): 341–360. doi:10.1007/s41060-020-00213-5
DOI: 10.1007/s41060-020-00213-5
Project(s): SoBigData
Metrics:
Sîrbu A., Andrienko G., Andrienko N., Boldrini C., Conti M., Giannotti F., Guidotti R., Bertoli S., Kim J., Muntean C. I., Pappalardo L., Passarella A., Pedreschi D., Pollacci L., Pratesi F., Sharma R.
How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.Source: International Journal of Data Science and Analytics (Online) 11 (2020): 341–360. doi:10.1007/s41060-020-00213-5
DOI: 10.1007/s41060-020-00213-5
Project(s): SoBigData
Metrics:
See at:
International Journal of Data Science and Analytics | link.springer.com | ISTI Repository | HAL Clermont Université | Fraunhofer-ePrints | CNR ExploRA
2020
Contribution to book
Open Access
"Know thyself" how personal music tastes shape the last.fm online social network
Guidotti R., Rossetti G.
As Nietzsche once wrote "Without music, life would be a mistake" (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.Source: Formal Methods. FM 2019 International Workshops Porto, Portugal, October 7-11, 2019, Revised Selected Papers, Part I, edited by Sekerinski E. et al., pp. 146–161, 2020
DOI: 10.1007/978-3-030-54994-7_11
Project(s): Track and Know, Track and Know , SoBigData
Metrics:
Guidotti R., Rossetti G.
As Nietzsche once wrote "Without music, life would be a mistake" (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.Source: Formal Methods. FM 2019 International Workshops Porto, Portugal, October 7-11, 2019, Revised Selected Papers, Part I, edited by Sekerinski E. et al., pp. 146–161, 2020
DOI: 10.1007/978-3-030-54994-7_11
Project(s): Track and Know
, Track and Know , SoBigData Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2019
Journal article
Open Access
Personalized market basket prediction with temporal annotated recurring sequences
Guidotti R., Rossetti G., Pappalardo L., Giannotti F., Pedreschi D.
Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.Source: IEEE transactions on knowledge and data engineering (Print) 31 (2019): 2151–2163. doi:10.1109/TKDE.2018.2872587
DOI: 10.1109/tkde.2018.2872587
Project(s): SoBigData
Metrics:
Guidotti R., Rossetti G., Pappalardo L., Giannotti F., Pedreschi D.
Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.Source: IEEE transactions on knowledge and data engineering (Print) 31 (2019): 2151–2163. doi:10.1109/TKDE.2018.2872587
DOI: 10.1109/tkde.2018.2872587
Project(s): SoBigData
Metrics:
See at:
Archivio della Ricerca - Università di Pisa | IEEE Transactions on Knowledge and Data Engineering | ISTI Repository | IEEE Transactions on Knowledge and Data Engineering | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
A survey of methods for explaining black box models
Guidotti R., Monreale A., Ruggieri S., Turini F., Giannotti F., Pedreschi D.
In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.Source: ACM computing surveys 51 (2019). doi:10.1145/3236009
DOI: 10.1145/3236009
DOI: 10.48550/arxiv.1802.01933
Project(s): SoBigData
Metrics:
Guidotti R., Monreale A., Ruggieri S., Turini F., Giannotti F., Pedreschi D.
In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.Source: ACM computing surveys 51 (2019). doi:10.1145/3236009
DOI: 10.1145/3236009
DOI: 10.48550/arxiv.1802.01933
Project(s): SoBigData
Metrics:
See at:
arXiv.org e-Print Archive | Archivio istituzionale della Ricerca - Scuola Normale Superiore | dl.acm.org | ACM Computing Surveys | Archivio della Ricerca - Università di Pisa | ISTI Repository | ACM Computing Surveys | doi.org | CNR ExploRA
2019
Journal article
Open Access
The italian music superdiversity. Geography, emotion and language: one resource to find them, one resource to rule them all
Pollacci L., Guidotti R., Rossetti G., Giannotti F., Pedreschi D.
Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs' melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.Source: Multimedia tools and applications (Dordrecht. Online) 78 (2019): 3297–3319. doi:10.1007/s11042-018-6511-6
DOI: 10.1007/s11042-018-6511-6
Project(s): SoBigData
Metrics:
Pollacci L., Guidotti R., Rossetti G., Giannotti F., Pedreschi D.
Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs' melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.Source: Multimedia tools and applications (Dordrecht. Online) 78 (2019): 3297–3319. doi:10.1007/s11042-018-6511-6
DOI: 10.1007/s11042-018-6511-6
Project(s): SoBigData
Metrics:
See at:
Multimedia Tools and Applications | Archivio della Ricerca - Università di Pisa | ISTI Repository | Multimedia Tools and Applications | link.springer.com | CNR ExploRA