Proceedings of the Korea Inteligent Information System Society Conference (한국지능정보시스템학회:학술대회논문집)
- 2007.05a
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- Pages.329-337
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- 2007
Two-Step Filtering Datamining Method Integrating Case-Based Reasoning and Rule Induction
- Park, Yoon-Joo (KOOKMIN University) ;
- Chol, En-Mi (KOOKMIN University) ;
- Park, Soo-Hyun (KOOKMIN University)
- Published : 2007.05.18
Abstract
Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and Rule Induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.
Keywords
- Hybrid method;
- Datamining;
- Case-Based Reasoning;
- Rule Induction;
- Artificial Intelligence;
- Credit Analysis;
- Medical Diagnosis