Enhancing perceptrons with contrastive biclusters

Abstract

An enhanced perceptron model, referred to as BicNeuron (BN), which is based on the novel concept of contrastive biclusters is investigated. According to this viewpoint, a coherent bicluster (i.e. a subset of data patterns showing high similarity across a particular subset of features) belonging to one class is required to show high discrimination from the nearest patterns of the other class when projected on the same feature set. On each local subspace associated with a different pair of contrastive biclusters a perceptron is induced and the model with highest area under the receiver operating characteristic curve value is selected as the final classifier. Experiments involving the discrimination of epileptic and non-epileptic electroencephalogram (EEG) signals show that the BN approach can be very useful, prevailing significantly on standard and kernel perceptrons in terms of classification accuracy.