Pareto front feature selection based on artificial bee colony optimization
journal contributionposted on 25.03.2021, 03:32 by E Hancer, Bing Xue, Mengjie Zhang, D Karaboga, B Akay
© 2017 Elsevier Inc. Feature selection has two major conflicting aims, i.e., to maximize the classification performance and to minimize the number of selected features to overcome the curse of dimensionality. To balance their trade-off, feature selection can be handled as a multi-objective problem. In this paper, a feature selection approach is proposed based on a new multi-objective artificial bee colony algorithm integrated with non-dominated sorting procedure and genetic operators. Two different implementations of the proposed approach are developed: ABC with binary representation and ABC with continuous representation. Their performance are examined on 12 benchmark datasets and the results are compared with those of linear forward selection, greedy stepwise backward selection, two single objective ABC algorithms and three well-known multi-objective evolutionary computation algorithms. The results show that the proposed approach with the binary representation outperformed the other methods in terms of both the dimensionality reduction and the classification accuracy.
Preferred citationHancer, E., Xue, B., Zhang, M., Karaboga, D. & Akay, B. (2018). Pareto front feature selection based on artificial bee colony optimization. Information Sciences, 422, 462-479. https://doi.org/10.1016/j.ins.2017.09.028
Journal titleInformation Sciences
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Science & TechnologyTechnologyComputer Science, Information SystemsComputer ScienceFeature selectionClassificationMulti-objective optimizationArtificial bee colonyPARTICLE SWARM OPTIMIZATIONEVOLUTIONARY ALGORITHMMUTUAL INFORMATIONCLASSIFICATIONArtificial Intelligence & Image ProcessingMathematical SciencesInformation and Computing SciencesEngineering