A Maximum K-Min Approach for Classification

AAAI 2013 pp. 1607-1608

doi:10.1609/AAAI.V27I1.8535 /aaai/2013/dong2013aaai-maximum/

Abstract

In this paper, a general Maximum K-Min approach for classification is proposed, which focuses on maximizing the gain obtained by the K worst-classified instances while ignoring the remaining ones. To make the original optimization problem with combinational constraints computationally tractable, the optimization techniques are adopted and a general compact representation lemma is summarized. Based on the lemma, a Nonlinear Maximum K-Min (NMKM) classifier is presented and the experiment results demonstrate the superior performance of the Maximum K-Min Approach.

PDF AAAI Semantic Scholar

Cite

Text

Dong and Yin. "A Maximum K-Min Approach for Classification." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8535

Markdown

[Dong and Yin. "A Maximum K-Min Approach for Classification." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/dong2013aaai-maximum/) doi:10.1609/AAAI.V27I1.8535

BibTeX

@inproceedings{dong2013aaai-maximum,
  title     = {{A Maximum K-Min Approach for Classification}},
  author    = {Dong, Mingzhi and Yin, Liang},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2013},
  pages     = {1607-1608},
  doi       = {10.1609/AAAI.V27I1.8535},
  url       = {https://mlanthology.org/aaai/2013/dong2013aaai-maximum/}
}