Worst-case analysis of rule discovery

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

2 被引用数 (Scopus)

抄録

In this paper, we perform a worst-case analysis of rule discovery. A rule is defined as a probabilistic constraint of true assignment to the class attribute of corresponding examples. In data mining, a rule can be considered as representing an important class of discovered patterns. We accomplish the aforementioned objective by extending a preliminary version of PAC learning, which represents a worst-case analysis for classification. Our analysis consists of two cases: the case in which we try to avoid finding a bad rule, and the case in which we try to avoid overlooking a good rule. Discussions on related works are also provided for PAC learning, multiple comparison, analysis of association rule discovery, and simultaneous reliability evaluation of a discovered rule.

本文言語英語
ホスト出版物のタイトルDiscovery Science - 4th International Conference, DS 2001, Proceedings
編集者Klaus P. Jantke, Ayumi Shinohara
出版社Springer Verlag
ページ365-377
ページ数13
ISBN(印刷版)9783540429562
DOI
出版ステータス出版済み - 2001
外部発表はい
イベント4th International Conference on Discovery Science, DS 2001 - Washington, 米国
継続期間: 11 25 200111 28 2001

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)
2226
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他4th International Conference on Discovery Science, DS 2001
国/地域米国
CityWashington
Period11/25/0111/28/01

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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