Multivariate Time Series Classification: A Relational Way
| Autor: | Dominique Gay, Alexis Bondu, Vincent Lemaire, Marc Boullé, Fabrice Clérot |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Multivariate statistics Selection (relational algebra) Computer science Relational database 05 social sciences Feature selection 02 engineering and technology computer.software_genre Bayesian inference Field (computer science) Activity recognition Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Data mining computer |
| Zdroj: | Big Data Analytics and Knowledge Discovery ISBN: 9783030590642 DaWaK |
| DOI: | 10.1007/978-3-030-59065-9_25 |
| Popis: | Multivariate Time Series Classification (MTSC) has attracted increasing research attention in the past years due to the wide range applications in e.g., action/activity recognition, EEG/ECG classification, etc. In this paper, we open a novel path to tackle with MTSC: a relational way. The multiple dimensions of MTS are represented in a relational data scheme, then a propositionalisation technique (based on classical aggregation/selection functions from the relational data field) is applied to build interpretable features from secondary tables to “flatten” the data. Finally, the MTS flattened data are classified using a selective Naive Bayes classifier. Experimental validation on various benchmark data sets show the relevance of the suggested approach. |
| Databáze: | OpenAIRE |
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