Abstract
Analysis of behavioral data represents today a big issue, as so many domains generate huge quantity of activity and mobility traces. When traces are labeled by the user that generates it, models can be learned to accurately predict the user of an unknown trace. In online systems however, users may have several virtual identities, or duplicate labels. By ignoring them, the prediction accuracy drastically drops, as the set of all virtual identities of a single person is not known beforehand. In this article, we tackle this duplicate labels identification problem, and present an original approach that explores the lattice of binary classifiers. Each subset of labels is learned as the positive class against the others (the negative class), and constraints make possible to identify duplicate labels while pruning the search space. We experiment this original approach with data of the video game Starcraft 2 in the new context of Electronic Sports (eSport) with encouraging results.
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Acknowledgments
This work has been partially financed by the projects FUI AAP 14 Tracaverre 2012–2016, VEL’INNOV (ANR INOV 2012) and GRAISearch (FP7-PEOPLE-2013-IAPP).
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Labernia, Q., Codocedo, V., Robardet, C., Kaytoue, M. (2017). Mining the Lattice of Binary Classifiers for Identifying Duplicate Labels in Behavioral Data. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_2
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DOI: https://doi.org/10.1007/978-3-319-60045-1_2
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