Approximate reduction from AUC maximization to 1-norm soft margin optimization

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

Finding linear classifiers that maximize AUC scores is important in ranking research. This is naturally formulated as a 1-norm hard/soft margin optimization problem over pn pairs of p positive and n negative instances. However, directly solving the optimization problems is impractical since the problem size (pn) is quadratically larger than the given sample size (p+n). In this paper, we give (approximate) reductions from the problems to hard/soft margin optimization problems of linear size. First, for the hard margin case, we show that the problem is reduced to a hard margin optimization problem over p+n instances in which the bias constant term is to be optimized. Then, for the soft margin case, we show that the problem is approximately reduced to a soft margin optimization problem over p+n instances for which the resulting linear classifier is guaranteed to have a certain margin over pairs.

本文言語英語
ホスト出版物のタイトルAlgorithmic Learning Theory - 22nd International Conference, ALT 2011, Proceedings
ページ324-337
ページ数14
DOI
出版ステータス出版済み - 2011
イベント22nd International Conference on Algorithmic Learning Theory, ALT 2011 - Espoo, フィンランド
継続期間: 10 5 201110 7 2011

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6925 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他22nd International Conference on Algorithmic Learning Theory, ALT 2011
国/地域フィンランド
CityEspoo
Period10/5/1110/7/11

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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