Optimisation of Classifier Ensemble for Predictive Toxicology Applications

Makhtar, Mokhairi, Yang, Longzhi, Neagu, Daniel and Ridley, Mick (2012) Optimisation of Classifier Ensemble for Predictive Toxicology Applications. In: 2012 UKSim 14th International Conference on Computer Modelling and Simulation (UKSim). IEEE, Piscataway, NJ, pp. 236-241. ISBN 978-1-4673-1366-7

Full text not available from this repository. (Request a copy)
Official URL: http://dx.doi.org/10.1109/UKSim.2012.41

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.

Item Type: Book Section
Uncontrolled Keywords: Classifier ensemble, classifiers ranking value, decision fusion strategy
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Longzhi Yang
Date Deposited: 07 Jan 2014 09:14
Last Modified: 10 Nov 2016 12:38
URI: http://nrl.northumbria.ac.uk/id/eprint/13092

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence