Study on Support Vector Machine Based Decision Tree and Application

Author

Dong, G.M. ; Chen, J.

Author_Institution

State Key Lab. of Mech. Syst. & Vibration, Shanghai Jiao Tong Univ., Shanghai

Volume

5

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

318

Lastpage

322

Abstract

Data mining has many topics such as classification, clustering, association, prediction, etc. Recently, classification problem is the research hotspot and decision tree is one of the most widely used classification methods, where C4.5 is one favorite algorithm. According to the disadvantages of conventional support vector machine (SVM), a SVM based decision tree (SVMDT) is introduced and modified by using equivalent distance as the class separability measure and includingthe consideration of "local class cluster" problem. At last the modified SVMDT is used to make diagnosis analysis of an experimental rotor kit.

Keywords

data mining; decision trees; support vector machines; classification problem; data mining; decision tree; support vector machine; Classification tree analysis; Clustering algorithms; Data mining; Decision trees; Machine learning; Partitioning algorithms; Risk management; Support vector machine classification; Support vector machines; Testing; C4.5 algorithm; Decision Tree; Multi-class SVM; Support Vector Machine; fault diagnosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on

Conference_Location

Jinan Shandong

Print_ISBN

978-0-7695-3305-6

Type

conf

DOI

10.1109/FSKD.2008.252

Filename

4666544