- Title
- Optimising weights for heterogeneous ensemble of classifiers with differential evolution
- Creator
- Haque, Mohammad Nazmul; Noman, M. Nasimul; Berretta, Regina; Moscato, Pablo
- Relation
- 2016 IEEE Congress on Evolutionary Computation (CEC). Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC) (Vancouver, Canada 24-29 July, 2016) p. 233-240
- Publisher Link
- http://dx.doi.org/10.1109/CEC.2016.7743800
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2016
- Description
- The classification performance of a weighted voting ensemble of classifiers largely depends on the proper weight chosen for each base classifier's vote. In this paper, we propose the use of Differential Evolution algorithm for adjustment of voting-weights of base classifiers used in a heterogeneous ensemble of classifiers (HEoC). We used the average Matthews Correlation Coefficient (MCC), calculated over 10-fold cross-validation, as the quality measure of an ensemble. We applied the vanilla DE algorithm to maximise the average MCC score over the training dataset. The algorithm optimises the base classifiers' voting weights in order to attain better generalisation performance of the ensemble on testing datasets. Experiments were performed using 10 binary-class datasets taken from UCI-Machine Learning Repository. The results show consistent and superior generalisation performance of the constructed ensembles when compared with the base classifiers and other well-known ensemble of classifiers.
- Subject
- pattern classification; evolutionary computation; artificial intelligence; learning
- Identifier
- http://hdl.handle.net/1959.13/1325616
- Identifier
- uon:25313
- Identifier
- ISBN:9781509006236
- Rights
- (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
- Language
- eng
- Full Text
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