Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction

• URL: http://arxiv.org/abs/2005.00259v1
• Date: Fri, 1 May 2020 07:46:29 GMT
• Title: Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction
• Authors: Shuchu Han, Alexandru Niculescu-Mizil
• Abstract summary: We introduce supervised feature ranking and feature subset selection algorithms for MTS classification. Unlike most existing supervised/unsupervised feature selection algorithms for MTS our techniques do not require a feature extraction step to generate a one-dimensional feature vector from the time series.
• Score: 78.84356269545157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce supervised feature ranking and feature subset selection algorithms for multivariate time series (MTS) classification. Unlike most existing supervised/unsupervised feature selection algorithms for MTS our techniques do not require a feature extraction step to generate a one-dimensional feature vector from the time series. Instead it is based on directly computing similarity between individual time series and assessing how well the resulting cluster structure matches the labels. The techniques are amenable to heterogeneous MTS data, where the time series measurements may have different sampling resolutions, and to multi-modal data.

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