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Formation of Nearest Neighbors Set Based on Similarity Threshold
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Jae Sik Lee (School of Business Administration, Ajou University)
Jin Chun Lee (Ubiquition Convergence Research Institute)
Vol. 13, No. 2, Page: 1 ~ 14
Keywords
Nearest Neighbors, Case-based Reasoning, Classification
Abstract
Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.
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유사도 임계치에 근거한 최근접 이웃 집합의 구성
이재식 (아주대학교 경영대학 e-비즈니스학부)
이진천 (유비쿼터스 컨버전스 연구소)
Abstract
사례기반추론은 다양한 예측 문제에 있어서 성공적으로 활용되고 있는 데이터 마이닝 기법 중 하나이다. 사례기반추론 시스템의 예측 성능은 예측에 사용되는 최근접 이웃 집합을 어떻게 구성하느냐에 따라 영향을 받게 된다. 최근접 이웃 집합의 구성에 있어서 대부분의 선행 연구들은 고정된 값인 K개의 사례를 포함시키는 k-NN 방법을 채택해왔다. 그러나 k-NN 방법을 채택하는 사례기반추론 시스템은 k 값을 너무 크게 혹은 작게 설정하게 되면 예측 성능이 저하된다. 본 연구에서는 이러한 문제를 해결하기 위해 최근접 이웃 집합을 구성함에 있어서 유사도의 임계치 자체를 이용하는 s-NN 방법을 제안하였다. UCI의 Machine Learning Repository에서 제공하는 데이터를 사용하여 실험한 결과, s-NN 방법을 적용한 사례기반추론 모델이 k-NN 방법을 적용한 사례기반추론 모델보다 더 우수한 성능을 보여주었다.
Cite this article
JIIS Style
Lee, J. S., and J. C. Lee, "Formation of Nearest Neighbors Set Based on Similarity Threshold ", Journal of Intelligence and Information Systems, Vol. 13, No. 2 (2007), 1~14.

IEEE Style
Jae Sik Lee, and Jin Chun Lee, "Formation of Nearest Neighbors Set Based on Similarity Threshold ", Journal of Intelligence and Information Systems, vol. 13, no. 2, pp. 1~14, 2007.

ACM Style
Lee, J. S., and Lee, J. C., 2007. Formation of Nearest Neighbors Set Based on Similarity Threshold . Journal of Intelligence and Information Systems. 13, 2, 1--14.
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@article{Lee:JIIS:2007:290,
author = {Lee, Jae Sik and Lee, Jin Chun},
title = {Formation of Nearest Neighbors Set Based on Similarity Threshold },
journal = {Journal of Intelligence and Information Systems},
issue_date = {June 2007},
volume = {13},
number = {2},
month = Jun,
year = {2007},
issn = {2288-4866},
pages = {1--14},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Nearest Neighbors, Case-based Reasoning and Classification },
}
%0 Journal Article
%1 290
%A Jae Sik Lee
%A Jin Chun Lee
%T Formation of Nearest Neighbors Set Based on Similarity Threshold
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 13
%N 2
%P 1-14
%D 2007
%R
%I Korea Intelligent Information System Society