Optimisation of ensemble classifiers using genetic algorithm

M. Gaber, Mohamed Bader

Research output: Contribution to conferencePaperpeer-review

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Abstract

Ensemble learning is a well established machine learning approach that utilises a number of classifiers to aggregate the decision about determining the class label. In its basic form this aggregation is achieved via majority voting. A generic approach, termed EV-Ensemble, for evolving a new ensemble from an existing one is proposed in this paper. This approach is applied to the high performance ensemble technique Random Forests. This study uses a genetic algorithm approach to further enhance the accuracy of Random Forests, based on the EV-Ensemble approach. The new technique is termed as Genetic Algorithm based Random Forests (GARF). Our extensive experimental study has proved that Random Forests performance could be boosted when evolved using the genetic algorithm approach.
Original languageEnglish
Publication statusPublished - 10 Sep 2012
EventProceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - San Sebastian, Spain
Duration: 10 Sep 201212 Sep 2012

Conference

ConferenceProceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
CountrySpain
CitySan Sebastian
Period10/09/1212/09/12

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