Feature subspace ensembles: A parallel classifier combination scheme using feature selection

Abstract

In feature selection (FS), different strategies usually lead to different results. Even the same strategy may do so in distinct feature selection contexts. We propose a feature subspace ensemble method, consisting on the parallel combination of decisions from multiple classifiers. Each classifier is designed using variations of the feature representation space, obtained by means of FS. With the proposed approach, relevant discriminative information contained in features neglected in a single run of a FS method, may be recovered by the application of multiple FS runs or algorithms, and contribute to the decision through the classifier combination process. Experimental results on benchmark data show that the proposed feature subspace ensembles method consistently leads to improved classification performance. © Springer-Verlag Berlin Heidelberg 2007.