1 research outputs found
Enhanced perceptrons using contrastive biclusters
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.Comment: article under review by Neural Computing and Applications, Springe