Optimisation of Classifier Ensemble for Predictive Toxicology Applications

Abstract

Ensembles of classifiers proved potential in getting higher accuracy compared to a single classifier. High diversity in an ensemble may improve the performance results significantly. We propose an ensemble approach which has diversity calculated using disagreement measure of classification output. A CRS (Classifier Ranking System) is introduced for the selection of relevant classifiers. We also propose the Optimisation of Classifiers Ensemble Method (OCEM) technique which applies to the ensemble selection. In this paper, we focus on classification models for predictive toxicology applications, for which computational models are required to replace in vivo experiments. The results show that our method performs well in selecting the relevant ensemble model to improve the prediction from a collection of classifiers.