Accepted Paper: FEARS: a FEature And Representation Selection approach for time series classification

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Alexis Bondu (Orange); Dominique Gay (Université de La Réunion); Vincent Lemaire (Orange); Marc Boulle (Orange Labs); Eole Cervenka (Orange)


This paper presents a method which extractsinformative features while selecting simultaneously adequate representations for Time Series Classification. This method simultaneously (i) selects alternative representations, such as derivatives, cumulative integrals, power spectrum ... (ii) and extracts informative features (via automatic variable construction) from the selected set of representations. The suggested approach is decomposed in three steps: (i) the original time series are transformed into several representations which are stored as relational data; (ii) then, a {regularized} propositionalisation method is applied in order to generate informative aggregate features; (iii) finally, a selective Naive Bayes classifier is learned from the outcoming feature-value data table. The previous steps are repeated by a forward backward selection algorithm in order to select the most informative subset of representations. The suggested approach proves to be highly competitive when compared with state-of-the-art methods while extracting interpretable features. Furthermore, the suggested approach is almost parameter free and only requires few hardware resources.