A Feature Selection Method for Multi-Dimension Time-Series Data

• URL: http://arxiv.org/abs/2104.11110v1
• Date: Thu, 22 Apr 2021 14:49:00 GMT
• Title: A Feature Selection Method for Multi-Dimension Time-Series Data
• Authors: Bahavathy Kathirgamanathan and Padraig Cunningham
• Abstract summary: Time-series data in application areas such as motion capture and activity recognition is often multi-dimension. There is a lot of redundancy in these data streams and good classification accuracy will often be achievable with a small number of features. We present a method for feature subset selection on multidimensional time-series data based on mutual information.
• Score: 2.055949720959582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time-series data in application areas such as motion capture and activity recognition is often multi-dimension. In these application areas data typically comes from wearable sensors or is extracted from video. There is a lot of redundancy in these data streams and good classification accuracy will often be achievable with a small number of features (dimensions). In this paper we present a method for feature subset selection on multidimensional time-series data based on mutual information. This method calculates a merit score (MSTS) based on correlation patterns of the outputs of classifiers trained on single features and the `best' subset is selected accordingly. MSTS was found to be significantly more efficient in terms of computational cost while also managing to maintain a good overall accuracy when compared to Wrapper-based feature selection, a feature selection strategy that is popular elsewhere in Machine Learning. We describe the motivations behind this feature selection strategy and evaluate its effectiveness on six time series datasets.

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