Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Editors | B. Orchard, C. Yang, M. Ali |
| Pages | 1072-1081 |
| Number of pages | 10 |
| Volume | 3029 |
| Publication status | Published - 2004 |
| Externally published | Yes |
| Event | 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada Duration: 2004 May 17 → 2004 May 20 |
Other
| Other | 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 |
|---|---|
| Country | Canada |
| City | Ottowa, Ont. |
| Period | 04/5/17 → 04/5/20 |
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ASJC Scopus subject areas
- Hardware and Architecture
Cite this
Comparison between objective interestingness measures and real human interest in medical data mining. / Ohsaki, Miho; Sato, Yoshinori; Kitaguchi, Shinya; Yokoi, Hideto; Yamaguchi, Takahira.
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / B. Orchard; C. Yang; M. Ali. Vol. 3029 2004. p. 1072-1081.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Comparison between objective interestingness measures and real human interest in medical data mining
AU - Ohsaki, Miho
AU - Sato, Yoshinori
AU - Kitaguchi, Shinya
AU - Yokoi, Hideto
AU - Yamaguchi, Takahira
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=9444268735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=9444268735&partnerID=8YFLogxK
M3 - Conference contribution
VL - 3029
SP - 1072
EP - 1081
BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
A2 - Orchard, B.
A2 - Yang, C.
A2 - Ali, M.
ER -