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

抜粋

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.

元の言語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)
3029
出版物ステータスPublished - 2004 12 9
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 172004 5 20

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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