Tools
Tools
Engen, V., Vincent, J., Schierz, A. C. and Phalp, K. T., 2009. Multi-Objective Evolution Of The Pareto Optimal Set Of Neural Network Classifier Ensembles. In: International Conference of Machine Learning and Cybernetics (ICMLC & ICWAPR)2009., 12-15 July 2009, Baoding, Hebei, China, pp. 74-79.
This is the latest version of this eprint.
Full text not available from this repository.
DOI: 10.1109/ICMLC.2009.5212485
Abstract
Existing research demonstrates that classifier ensem- bles can improve on the performance of the single ‘best’ classifier. However, for some problems, although the ensemble may obtain a lower classification error than any of the base classifiers, it may not provide the de- sired trade-off among the classification rates of differ- ent classes. In many applications, classes are not of equal importance, but the preferred trade-off may be hard to quantify a priori. In this paper, we adopt multi- objective techniques to create Pareto optimal sets of classifiers and ensembles, offering the user the choice of preferred trade-off. We also demonstrate that the common practice of developing a single ensemble from an arbitrary (diverse) selection of base classifiers will be inferior to a large proportion of those classifiers.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Generalities > Computer Science and Informatics > Artificial Intelligence |
| Group: | School of Design, Engineering & Computing > Smart Technology Research Centre School of Design, Engineering & Computing > Software Systems Research Centre |
| ID Code: | 12377 |
| Deposited By: | Dr Amanda C. Schierz LEFT |
| Deposited On: | 09 Dec 2009 17:26 |
| Last Modified: | 07 Mar 2013 15:18 |
Available Versions of this Item
| Repository Staff Only - | |
| BU Staff Only - | |
| Help Guide - | Editing Your Items in BURO |