Hierarchical Feature Subset Selection for Features Computed from the Continuous Wavelet Transform

An algorithm for feature subset selection is proposed in which the correlation structure of the features is exploited. Especially in pattern recognition applications when features are computed from the continuous wavelet transform features are highly correlated and the algorithm is shown to be performing better. The algorithm is a hybrid filter/wrapper approach for feature subset selection. The filter removes irrelevant and redundant features. The wrapper part of the algorithm can be conceived as a hierarchical search for features: a search at the cluster level followed by a search at within-cluster level. It is shown that a significant increase in performance for the ACO (ant colony optimization) and the GA (genetic algorithm) optimization algorithms are obtained, both examples of meta heuristic optimization algorithms. However our approach is not limited to meta-heuristic search algorithms. Essentially any search algorithm can be plugged into the proposed algorithm