Hong, Chengyu; Zhang, Xuefeng; Wang, Yutong A classification method of SVM based on lower approximation theory of rough set. (Chinese. English summary) Zbl 1199.68214 J. Qufu Norm. Univ., Nat. Sci. 34, No. 2, 33-36 (2008). Summary: The traditional rough set theory can not deal with the continuous attribute, and the classification rules which are obtained from the rough set are mostly complicated. Though SVM can get the concise classification rules and can deal with the continuous attribute, but it can only be used for small samples. This paper presents a SVM classification method based on rough set’s lower approximation theory and its application in continuous attribute. Experimental results show that the method can preserve the necessary information needed by SVM, improve the prediction accuracy and reduce the training time of support vector machine. MSC: 68T05 Learning and adaptive systems in artificial intelligence 68T37 Reasoning under uncertainty in the context of artificial intelligence 03E72 Theory of fuzzy sets, etc. Keywords:rough set; support vector machine; lower approximation PDFBibTeX XMLCite \textit{C. Hong} et al., J. Qufu Norm. Univ., Nat. Sci. 34, No. 2, 33--36 (2008; Zbl 1199.68214)