  Correlations in streams of multivariate time series data means that
typically, only a small subset of the features are required for a given data
mining task. In this paper, we propose a technique which we call Merit Score
for Time-Series data (MSTS) that does feature subset selection based on the
correlation patterns of single feature classifier outputs. We assign a Merit
Score to the feature subsets which is used as the basis for selecting 'good'
feature subsets. The proposed technique is evaluated on datasets from the UEA
multivariate time series archive and is compared against a Wrapper approach for
feature subset selection. MSTS is shown to be effective for feature subset
selection and is in particular effective as a data reduction technique. MSTS is
shown here to be computationally more efficient than the Wrapper strategy in
selecting a suitable feature subset, being more than 100 times faster for some
larger datasets while also maintaining a good classification accuracy.

Machine Learning