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논문 기본 정보

자료유형
학술대회자료
저자정보
Hyunchul Ahn (Korea Institute for Defense Analyses) Kyoung-jae Kim (Dongguk University)
저널정보
한국지능정보시스템학회 한국지능정보시스템학회 학술대회논문집 한국지능정보시스템학회 2007년 춘계학술대회 논문집
발행연도
2007.5
수록면
389 - 398 (10page)

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초록· 키워드

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Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

목차

Abstract
Introduction
Prior Research
Global Optimization of CBR using GA
The Research Design and Experiments
Experimental Results
Conclusions
References

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UCI(KEPA) : I410-ECN-0101-2009-003-019032120