Paper Info
Title
A Feature Selection Method for Multi-dimension Time-Series Data.
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.
Year
DOI
Venue
2020
10.1007/978-3-030-65742-0_15
AALTD@PKDD/ECML
DocType
ISSN
Citations 
Conference
In: Advanced Analytics and Learning on Temporal Data. AALTD 2020. LNCS, vol 12588. Springer, Cham (2020)
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Bahavathy Kathirgamanathan100.68
Pádraig Cunningham23086218.37