基于随机子空间的最小最大模块化支持向量机<sup>*</sup>

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Abstract The min-max modular support vector machine (M3-SVM) is a powerful tool for dealing with large-scale data. To improve the classification performance of M3-SVM for unblanced data with high dimension, several random subspace strategies are analyzed and combined with M3-SVM to reduce the dimensionality and add the ensemble mechanism on feature level. Thus, the min-max modular support vector machine with random subspace is proposed. The experimental results on real-world datasets including unbalanced data indicate that the proposed random subspace strategy enhances the classification of M3-SVM. Moreover, the diversity between sub-modules (base learner) in M3-SVM is discussed.

Key words： Random Subspace (RS) Min-Max Modular Support Vector Machine (M3-SVM) Diversity

Received: 13 May 2013

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	YU Yi
	WU Jiao-Gao
	LI Yun

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YU Yi,WU Jiao-Gao,LI Yun. Integration of Random Subspace and Min-Max Modular SVM[J]. , 2014, 27(2): 153-159.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2014/V27/I2/153

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