NDLI: Decision Tree Support Vector Machine based on Genetic Algorithm for fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Qiang Wang Huanhuan Chen Yi Shen
Copyright Year	2008
Description	Author affiliation: Dept. of Control Sci. & Eng., Harbin Inst. of Technol., Harbin (Qiang Wang; Huanhuan Chen; Yi Shen)
Abstract	Decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed to solve the multi-class fault diagnosis tasks. Since the classification performance of DTSVM is closely related to its structure, genetic algorithm is introduced into the formation of decision tree, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, so that the most separable classes would be separated at each node of decision tree. The results of numerical simulations conducted on three datasets compared with ldquoone-against-allrdquo and ldquoone-against-onerdquo, show that the proposed method has better performance and higher generalization ability than the two conventional methods.
Starting Page	2668
Ending Page	2672
File Size	129406
Page Count	5
File Format	PDF
ISBN	9781424425020
DOI	10.1109/ICAL.2008.4636624
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Fault diagnosis Support vector machine (SVM) Genetic algorithm Classification algorithms Decision trees Decision tree Biological cells Genetic algorithms Classification tree analysis
Content Type	Text
Resource Type	Article

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