Development of a Novel Feature Weighting Method Using CMA-ES Optimization
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Feature 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.
Açıklama
26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY
Gokalp, Osman/0000-0002-7604-8647;
Gokalp, Osman/0000-0002-7604-8647;
Anahtar Kelimeler
machine learning, pattern recognition, feature weighting, feature selection, optimization, classification, ensemble learning, data mining
Kaynak
2018 26Th Signal Processing and Communications Applications Conference (Siu)
WoS Q Değeri
N/A
