An Improved Random Subspace Method and Its Application to EEG Signal Classification

Abstract

Ensemble learning is one of the principal current directions in the research of machine learning. In this paper, subspace ensembles for classification are explored which constitute an ensemble classifier system by manipulating different feature subspaces. Starting with the nature of ensemble efficacy, we probe into the microcosmic meaning of ensemble diversity, and propose to use region partitioning and region weighting to implement effective subspace ensembles. An improved random subspace method that integrates this mechanism is presented. Individual classifiers possessing eminent performance on a partitioned region reflected by high neighborhood accuracies, are deemed to contribute largely to this region, and are assigned large weights in determining the labels of instances in this area. The robustness and effectiveness of the proposed method is shown empirically with the base classifier of linear support vector machines on the classification problem of EEG signals.