Paper Info
Title
Support vector machine classification for large data sets via minimum enclosing ball clustering
Abstract
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.
Year
DOI
Venue
2008
10.1016/j.neucom.2007.07.028
Neurocomputing
Keywords
Field
DocType
data classification,svm classifier,training data,novel svm classification approach,normal svm,ball clustering,large data set,good classification accuracy,classic svm,time svm classification,support vector machine classification,high classification accuracy,support vector machine,support vector,classification
Structured support vector machine,Data set,One-class classification,Ranking SVM,Pattern recognition,Support vector machine,Artificial intelligence,Data classification,Cluster analysis,Support vector machine classification,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
71
4-6
Neurocomputing
Citations 
PageRank 
References 
45
1.78
21
Authors
4
Name
Order
Citations
PageRank
Jair Cervantes117618.08
Xiaoou Li255061.95
Wen Yu328322.70
Kang Li445037.45