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Fuzzy k-NN Based Classifiers for Time Series with Soft Labels

Abstract : Time series are temporal ordered data available in many fields of science such as medicine, physics, astronomy, audio, etc. Various methods have been proposed to analyze time series. Amongst them, time series classification consists in predicting the class of a time series according to a set of already classified data. However, the performance of a time series classification algorithm depends on the quality of the known labels. In real applications, time series are often labeled by an expert or by an imprecise process, leading to noisy classes. Several algorithms have been developed to handle uncertain labels in case of non-temporal data sets. As an example, the fuzzy k-NN introduces for labeled objects a degree of membership to belong to classes. In this paper, we combine two popular time series classification algorithms, Bag of SFA Symbols (BOSS) and the Dynamic Time Warping (DTW) with the fuzzy k-NN. The new algorithms are called Fuzzy DTW and Fuzzy BOSS. Results show that our fuzzy time series classification algorithms outperform the non-soft algorithms especially when the level of noise is high.
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https://hal.inrae.fr/hal-02900605
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Submitted on : Thursday, July 16, 2020 - 11:37:14 AM
Last modification on : Thursday, August 5, 2021 - 3:52:36 AM

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Nicolas Wagner, Violaine Antoine, Jonas Koko, Romain Lardy. Fuzzy k-NN Based Classifiers for Time Series with Soft Labels. 18. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 2020, Lisbon, Portugal. pp.578-589, ⟨10.1007/978-3-030-50153-2_43⟩. ⟨hal-02900605⟩

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