DocumentCode :
999429
Title :
Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
Author :
Liu, Bo ; Hao, Zhifeng ; Tsang, Eric C C
Volume :
19
Issue :
12
fYear :
2008
Firstpage :
2044
Lastpage :
2052
Abstract :
Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.
Keywords :
Multiclass classification algorithms; one-against-one algorithm; support vector machines (SVMs); Algorithms; Artificial Intelligence; Computer Simulation; Models, Theoretical; Pattern Recognition, Automated;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2008.2003298
Filename :
4682646
Link To Document :
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