A CLASS-INCREMENTAL LEARNING METHOD FOR MULTI-CLASS SUPPORT VECTOR MACHINES IN TEXT CLASSIFICATION
To solve multi-class problems of support vector machines(SVM) more efficiently, a novel framework, which we call class-incremental learning (CIL), is proposed in this paper.CIL consists of two phases: incremental feature selection and incremental training, for updating the knowledge of old SVM classifiers in text classification when new classes are added to the system. CIL reuses the old models of the classifier and learns only one binary sub-classifier with an additional phase of feature selection when a new class comes. In the testing phase, current classifier is applied to the vectors projections on the sub-spaces concerned. CIL can serve as a flexible approach for all binary classification algorithms in text classification. Our experiment shows that the CIL-based SVM was not only substantially faster in training time than the popular batch SVM learning methods such as 1-against-rest,1-against-1 and divide-by-2 but also almost competed to the best performances in effectiveness of them.
Machine learning class-incremental learning support vector machines text classification feature selection Internet information filtering
BO-FENG ZHANG JIN-SHU SU XIN XU
School of Computer, National University of Defense Technology, Changsha 410073, China School of Mechantronics Engineering and Automation, National Univ.of Defense Tech., Changsha 410073,
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2581-2585
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）