期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：1
出版社：IJCSI Press
摘要：The support vector machine recursive feature elimination (SVM-RFE) is one of the most effective feature selection methods which has been successfully used in selecting informative genes for cancer classification. This paper extends this well-studied algorithm to the privacy preserving distributed data mining issue. For gene selection over multiple patient data from different sites, we propose a novel RFE-SVM method which aims to learn global informative gene subset to get the highest cancer classification accuracy, with limits on sharing of information. We experiment it using Leukemia bio-medical dataset. The experimental results show that it can provide good capability of privacy preserving and generates a set of attributes that is very similar to the set produced by its centralized counterpart.
关键词：Privacy Preserving; Gene selection; Distributed Data Mining; RFE;SVM; Cancer Diagnostic.
