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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
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
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