Comparison between objective interestingness measures and real human interest in medical data mining

Miho Ohsaki; Yoshinori Sato; Shinya Kitaguchi; Hideto Yokoi; Takahira Yamaguchi

doi:10.1007/978-3-540-24677-0_110

Comparison between objective interestingness measures and real human interest in medical data mining

Miho Ohsaki, Yoshinori Sato, Shinya Kitaguchi, Hideto Yokoi, Takahira Yamaguchi

研究成果: Conference article › 査読

11 被引用数 (Scopus)

抄録

This research empirically investigates the performance of conventional rule interestingness measures and discusses their availability to supporting KDD through system-human interaction in medical domain. We compared the evaluation results by a medical expert and that by selected measures for the rules discovered from a dataset on hepatitis. Recall and ² Measure 1 demon-strated the highest performance, and all measures showed different trends under our experimental conditions. These results indicated that some measures can predict really interesting rules at a certain level and that their combinational use in system-human interaction will be useful.

本文言語	English
ページ（範囲）	1072-1081
ページ数	10
ジャーナル	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
巻	3029
DOI	https://doi.org/10.1007/978-3-540-24677-0_110
出版ステータス	Published - 2004
外部発表	はい
イベント	17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada 継続期間: 2004 5 17 → 2004 5 20

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

文献へのアクセス

10.1007/978-3-540-24677-0_110

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引用スタイル

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