A Classifier Ensemble Enriched with Unsupervised Learning

Abstract

A novel methodology has been suggested to automatically recognize mine in SONAR data. The suggested framework employs a possibilistic ensemble method to classify SONAR instances as mine or mine-like object. The suggested algorithm minimizes an objective function that merges background identification, multi-algorithm fusion criteria and a learning term. The optimization wants to discover backgrounds as solid clusters in subspaces of the high-dimensional feature-space via a possibilistic semi-supervised learning and feature discrimination. The proposed clustering element allocates a degree of typicality to each data instance in order to recognize and decrease the power of noise instances and outliers. After that the approach results in optimal fusion parameters for each background. The trials on artificial datasets and standard SONAR dataset show that our proposed ensemble does better than individual classifiers and unsupervised local fusion.