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Conference papers

Temporal and Frequential Metric Learning for Time Series kNN Classification

Abstract : In order to classify time series, many machine learning algorithms such as the kNN classifer require a metric. We propose in this work a framework to learn a combination of multiple metrics for a robust kNN classifier. This combined metric includes both temporal (value and behavior) and frequential components. By introducing the concept of pairwise space, the combination function is learned in this new space through a "large margin" optimization process. The efficiency of the learned metric is compared to the major alternative metrics on large public datasets.
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Contributor : Ahlame Douzal <>
Submitted on : Friday, October 2, 2015 - 3:34:39 PM
Last modification on : Wednesday, March 31, 2021 - 4:54:35 PM


  • HAL Id : hal-01208429, version 1



Cao-Tri Do, Ahlame Douzal-Chouakria, Sylvain Marié, Michèle Rombaut. Temporal and Frequential Metric Learning for Time Series kNN Classification. ECML-PKDD 2015 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database, Sep 2015, Porto, Portugal. ⟨hal-01208429⟩



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