Classification of brain glioma by using SVMs bagging with feature selection

Authors:
Guo-Zheng Li;Tian-Yu Liu;Victor S. Cheng
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China;Institute of Biomedical Instrument, Shanghai Jiao Tong University, Shanghai, China
Venue:
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Year:
2006

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Abstract

The degree of malignancy in brain glioma needs to be assessed by MRI findings and clinical data before operations. There have been previous attempts to solve this problem by using fuzzy max-min neural networks and support vector machines (SVMs), while in this paper, a novel algorithm named PRIFEB is proposed by combining bagging of SVMs with embedded feature selection for its individuals. PRIFEB is compared with the general case of bagging on UCI data sets, experimental results show PRIFEB can obtain better performance than the general case of bagging. Then, PRIFEB is used to predict the degree of malignancy in brain glioma, computation results show that PRIFEB obtains better accuracy than other several methods like bagging of SVMs and single SVMs does.