The purpose of motion: learning activities from individual mobility networks
Rinzivillo S., Gabrielli L., Nanni M., Pappalardo L., Pedreschi D., Giannotti F.
Complex networks Semantics
The large availability of mobility data allows us to investigate complex phenomena about human movement. However this adundance of data comes with few information about the purpose of movement. In this work we address the issue of activity recognition by introducing Activity-Based Cascading (ABC) classification. Such approach departs completely from probabilistic approaches for two main reasons. First, it exploits a set of structural features extracted from the Individual Mobility Network (IMN), a model able to capture the salient aspects of individual mobility. Second, it uses a cascading classification as a way to tackle the highly skewed frequency of activity classes. We show that our approach outperforms existing state-of-theart probabilistic methods. Since it reaches high precision, ABC classification represents a very reliable semantic amplifier for Big Data.
Source: DSAA 2014 - The 2014 International Conference on Data Science and Advanced Analytics, Shanghai, China, 30 October - 1 November 2014
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@inproceedings{oai:it.cnr:prodotti:308070,
title = {The purpose of motion: learning activities from individual mobility networks},
author = {Rinzivillo S. and Gabrielli L. and Nanni M. and Pappalardo L. and Pedreschi D. and Giannotti F.},
booktitle = {DSAA 2014 - The 2014 International Conference on Data Science and Advanced Analytics, Shanghai, China, 30 October - 1 November 2014},
year = {2014}
}
0000-0003-1208-8321