Scopus
A multi-objective artificial immune algorithm for parameter optimization in support vector machine
Applied Soft Computing Journal · Ocak 2011
Makale Bilgileri
DergiApplied Soft Computing Journal
Yayın TarihiOcak 2011
Cilt / Sayfa11 · 120-129
Scopus ID2-s2.0-77957929022
Özet
Support vector machine (SVM) is a classification method based on the structured risk minimization principle. Penalize, C; and kernel, σ parameters of SVM must be carefully selected in establishing an efficient SVM model. These parameters are selected by trial and error or man's experience. Artificial immune system (AIS) can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper. In training stage of SVM, multiple solutions are found by using multi-objective artificial immune algorithm and then these parameters are evaluated in test stage. The proposed algorithm is applied to fault diagnosis of induction motors and anomaly detection problems and successful results are obtained. © 2010 Elsevier B.V. All rights reserved.
Yazarlar (3)
1
Ilhan Aydin
ORCID: 0000-0001-6880-4935
2
Mehmet Karaköse
3
Erhan Akin
ORCID: 0000-0001-6476-9255
Anahtar Kelimeler
Anomaly detection
Artificial immune system
Fault diagnosis
Optimization
Support vector machine
Kurumlar
Firat Üniversitesi
Elazig Turkey
Metrikler
195
Atıf
3
Yazar
5
Anahtar Kelime