Journal article
Optimal ensemble construction via meta-evolutionary ensembles
Expert systems with applications, Vol.30(4), pp.705-714
05/01/2006
DOI: 10.1016/j.eswa.2005.07.030
Abstract
In this paper, we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. Experimental results on 15 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods.
Details
- Title: Subtitle
- Optimal ensemble construction via meta-evolutionary ensembles
- Creators
- YongSeog Kim - Utah State UniversityW. Nick Street - University of IowaFilippo Menczer - Indiana University
- Resource Type
- Journal article
- Publication Details
- Expert systems with applications, Vol.30(4), pp.705-714
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.eswa.2005.07.030
- ISSN
- 0957-4174
- eISSN
- 1873-6793
- Language
- English
- Date published
- 05/01/2006
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380514202771
Metrics
9 Record Views