Fuzzy k-NN Based Classifiers for Time Series with Soft Labels
| Autor: | Jonas Koko, Nicolas Wagner, Violaine Antoine, Romain Lardy |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Dynamic time warping
Series (mathematics) business.industry Process (computing) BOSS Pattern recognition 02 engineering and technology [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] Fuzzy k-NN Fuzzy logic Class (biology) Article Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Boss Soft labels 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Time series classification Artificial intelligence Noise (video) business |
| Zdroj: | Communications in Computer and Information Science 18. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems 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⟩ Information Processing and Management of Uncertainty in Knowledge-Based Systems Information Processing and Management of Uncertainty in Knowledge-Based Systems ISBN: 9783030501525 IPMU (3) |
| DOI: | 10.1007/978-3-030-50153-2_43⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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