A Maximum K-Min Approach for Classification

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 Intel Labs China
  • Jun Guo Beijing University of Posts and Telecommunications
  • Honggang Zhang Beijing University of Posts and Telecommunications

DOI:

https://doi.org/10.1609/aaai.v27i1.8635

Keywords:

Classification, Maximum K Min

Abstract

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 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 classification 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.

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Published

2013-06-30

How to Cite

Dong, M., Yin, L., Deng, W., Shang, L., Guo, J., & Zhang, H. (2013). A Maximum K-Min Approach for Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 246-252. https://doi.org/10.1609/aaai.v27i1.8635