FEARS: a Feature and Representation Selection approach for Time Series Classification

Alexis Bondu, Dominique Gay, Vincent Lemaire, Marc Boullé, Eole Cervenka
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:379-394, 2019.

Abstract

This paper presents a method which extracts informative 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v101-bondu19a, title = {FEARS: a Feature and Representation Selection approach for Time Series Classification}, author = {Bondu, Alexis and Gay, Dominique and Lemaire, Vincent and Boull\'e, Marc and Cervenka, Eole}, pages = {379--394}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/bondu19a/bondu19a.pdf}, url = {http://proceedings.mlr.press/v101/bondu19a.html}, abstract = {This paper presents a method which extracts informative 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.} }
Endnote
%0 Conference Paper %T FEARS: a Feature and Representation Selection approach for Time Series Classification %A Alexis Bondu %A Dominique Gay %A Vincent Lemaire %A Marc Boullé %A Eole Cervenka %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-bondu19a %I PMLR %J Proceedings of Machine Learning Research %P 379--394 %U http://proceedings.mlr.press %V 101 %W PMLR %X This paper presents a method which extracts informative 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.
APA
Bondu, A., Gay, D., Lemaire, V., Boullé, M. & Cervenka, E.. (2019). FEARS: a Feature and Representation Selection approach for Time Series Classification. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:379-394

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