Computer Science ›› 2016, Vol. 43 ›› Issue (12): 135-138.doi: 10.11896/j.issn.1002-137X.2016.12.024

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Rough Set One-class Support Vector Machine Based on Within-class Scatter

ZHANG Bin and ZHU Jia-gang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Classical rough one-class support vector machine(ROC-SVM) constructs rough upper margin and rough lo-wer margin to deal with the over-fitting problem on rough set theory.However,in the process of searching for the optimal classification hyper-plane,ROC-SVM ignores the inner-class structure of the training data which is a very important prior knowledge.Thus,a rough set one-class support vector machine based on within-class scatter(WSROC-SVM) was proposed.This algorithm optimizes the inner-class structure of the training data by minimizing the within-class scatter of the training data.It not only precipitates margin between the origin and the training data in a higher dimensional space as large as possible,but also makes the training data close around the rough upper margin as tight as possible.Experimental results carried out on one synthetic dataset and the UCI dataset indicate that the proposed method improves the accuracy as well as the generalization of the result.And it is more advantageous in solving practical classification problems.

Key words: Rough set,One-class SVM,Within-class scatter,Over-fitting

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