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

TL;DR

This paper introduces a mutual information-based feature selection method for multi-dimensional time-series data, reducing redundancy and computational cost while maintaining high classification accuracy.

Contribution

It proposes MSTS, a novel feature subset selection technique that improves efficiency over wrapper methods for multi-dimensional time-series data.

Findings

MSTS is more computationally efficient than wrapper methods.

MSTS maintains high classification accuracy.

Effective on six diverse time series datasets.

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.