A fast feature selection approach based on extreme learning machine and coefficient of variation
Yükleniyor...
Dosyalar
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Tubıtak scıentıfıc & technıcal research councıl turkey, ataturk bulvarı no 221, kavaklıdere, ankara, 00000, turkey
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Feature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.
Açıklama
Anahtar Kelimeler
Classıfıcatıon, optımızatıon
Kaynak
Turkısh journal of electrıcal engıneerıng and computer scıences
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
25
Sayı
4
Künye
Ertugrul, OF. Tagluk, ME . (2017).
A fast feature selection approach based on extreme learning machine and coefficient of variation.Cilt:25. Sayı:4. 3409-3420 ss.
