  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.
