Development of a Novel Feature Weighting Method Using CMA-ES Optimization

26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYGokalp, Osman/0000-0002-7604-8647;WOS:000511448500031Feature weighting is one of the fundamental problems in machine learning algorithms and data mining to determine the importance of features. in this study, a novel feature weighting method using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization method for classification process is proposed. Experimental results are obtained by 10-fold cross validation technique with 3 different classifier models: Naive Bayes (NB), K nearest neighbors (K-NN) and Random Forest (RF) on 5 datasets in UCI Machine Learning Repository. Classification accuracy rate is used as the performance criterion. in addition, the developed CMAES-based method is also adapted to optimize these 3 classifiers in a voting-based ensemble algorithm. in this context, a different ensemble-based method is presented with CMAES-based feature weights obtained when the classifiers are individually and all together. Experimental studies show that the developed method gives better performance and promising results than the results obtained without feature weighting.IEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Uni

Development of a novel feature weighting method using CMA-ES optimization [CMA-ES Eniyilemesi Kullanilarak Özgün Bir Öznitelik Agirliklandirma Yöntemi Gelistirilmesi]

Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- 137780Feature weighting is one of the fundamental problems in machine learning algorithms and data mining to determine the importance of features. In this study, a novel feature weighting method using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization method for classification process is proposed. Experimental results are obtained by 10-fold cross validation technique with 3 different classifier models: Naive Bayes (NB), K nearest neighbors (K-NN) and Random Forest (RF) on 5 datasets in UCI Machine Learning Repository. Classification accuracy rate is used as the performance criterion. In addition, the developed CMAES-based method is also adapted to optimize these 3 classifiers in a voting-based ensemble algorithm. In this context, a different ensemble-based method is presented with CMAES-based feature weights obtained when the classifiers are individually and all together. Experimental studies show that the developed method gives better performance and promising results than the results obtained without feature weighting. © 2018 IEEE