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) |
| 巻 | 3029 |
| DOI | |
| 出版物ステータス | Published - 2004 |
| 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)
フィンガープリント Comparison between objective interestingness measures and real human interest in medical data mining' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。
これを引用
Ohsaki, M., Sato, Y., Kitaguchi, S., Yokoi, H., & Yamaguchi, T. (2004). Comparison between objective interestingness measures and real human interest in medical data mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3029, 1072-1081. https://doi.org/10.1007/978-3-540-24677-0_110