Enhanced perceptrons using contrastive biclusters

TL;DR

This paper introduces an enhanced perceptron model that leverages contrastive biclusters to improve classification performance, especially in complex real-world datasets, outperforming standard perceptrons in accuracy and AUC.

Contribution

The paper proposes a novel perceptron variant based on contrastive biclusters, combining local subspace training and model selection to enhance discriminative capability.

Findings

Outperforms standard perceptrons in accuracy and AUC on various datasets.

Effective on complex biosignal classification problems.

Utilizes local subspace training for improved discrimination.

Abstract

Perceptrons are neuronal devices capable of fully discriminating linearly separable classes. Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. Therefore, several approaches, such as kernel perceptrons, have been conceived to counteract such difficulties. In this paper, we investigate an enhanced perceptron model based on the notion of contrastive biclusters. From this perspective, a good discriminative bicluster comprises a subset of data instances belonging to one class that show high coherence across a subset of features and high differentiation from nearest instances of the other class under the same features (referred to as its contrastive bicluster). Upon each local subspace associated with a pair of contrastive biclusters a perceptron is trained and the model with highest area under the receiver operating characteristic curve (AUC) value is selected as the final classifier. Experiments conducted on a range of data sets, including those related to a difficult biosignal classification problem, show that the proposed variant can be indeed very useful, prevailing in most of the cases upon standard and kernel perceptrons in terms of accuracy and AUC measures.