A New Multi-class SVM Algorithm Based on One-Class SVM

Authors:
Xiao-Yuan Yang;Jia Liu;Min-Qing Zhang;Ke Niu
Affiliations:
Network and Information Security Key Laboratory, Engineering College of the Armed Police Forces, Xi'an 710086, China;Network and Information Security Key Laboratory, Engineering College of the Armed Police Forces, Xi'an 710086, China;Network and Information Security Key Laboratory, Engineering College of the Armed Police Forces, Xi'an 710086, China;Network and Information Security Key Laboratory, Engineering College of the Armed Police Forces, Xi'an 710086, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Abstract

Multi-class classification is an important and on-going research subject in machine learning and data mining. In this paper, we propose a new support vector algorithm, called OC-K-SVM, for multi-class classification based on one-class SVM. For k-class problem, this method constructs k classifiers, where each one is trained on data from one class. OC-K-SVM has parameters that enable us to control the number of support vectors and margin errors effectively, which is helpful in improving the accuracy of each classifier. We give some theoretical results concerning the significance of the parameters and show the robustness of classifiers. In addition, we have examined the proposed algorithm on several benchmark data sets, and our preliminary experiments confirm our theoretical conclusions.