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Generating diverse and accurate classifier ensembles using multi-objective optimization

Gu, S and Jin, Y (2015) Generating diverse and accurate classifier ensembles using multi-objective optimization IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - MCDM 2014: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Proceedings. pp. 9-15.

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Abstract

Accuracy and diversity are two vital requirements for constructing classifier ensembles. Previous work has achieved this by sequentially selecting accurate ensemble members while maximizing the diversity. As a result, the final diversity of the members in the ensemble will change. In addition, little work has been reported on discussing the trade-off between accuracy and diversity of classifier ensembles. This paper proposes a method for generating ensembles by explicitly maximizing classification accuracy and diversity of the ensemble together using a multi-objective evolutionary algorithm. We analyze the Pareto optimal solutions achieved by the proposed algorithm and compare them with the accuracy of single classifiers. Our results show that by explicitly maximizing diversity together with accuracy, we can find multiple classifier ensembles that outperform single classifiers. Our results also indicate that a combination of proper methods for creating and measuring diversity may be critical for generating ensembles that reliably outperform single classifiers.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Gu, SUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 12 January 2015
Identification Number : 10.1109/MCDM.2014.7007182
Additional Information : © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 24 Mar 2015 11:09
Last Modified : 23 May 2015 13:35
URI: http://epubs.surrey.ac.uk/id/eprint/807321

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