A Maximum K-Min Approach for Classification

In this paper, a general Maximum K-Min approach for classification is proposed. With the physical meaning of optimizing the classification confidence of the K worst instances, Maximum K-Min Gain/Minimum K- Max Loss (MKM) criterion is introduced. To make the original optimization problem with combinational number of constraints computationally tractable, the optimization techniques are adopted and a general compact representation lemma for MKM Criterion is summarized. Based on the lemma, a Nonlinear Maximum K- Min (NMKM) classifier and a Semi-supervised Maximum K-Min (SMKM) classifier are presented for traditional classification task and semi-supervised classi- fication task respectively. Based on the experiment results of publicly available datasets, our Maximum K- Min methods have achieved competitive performance when comparing against Hinge Loss classifiers.

Authors

• Mingzhi Dong Beijing University of Posts and Telecommunications
• Liang Yin Beijing University of Posts and Telecommunications
• Weihong Deng Beijing University of Posts and Telecommunications
• Li Shang
• Jun Guo
• Honggang Zhang School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China

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