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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제22권 제4호
- 발행연도
- 2020.1
- 수록면
- 1,303 - 1,312 (10page)
이용수
초록· 키워드
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Nearest neighbor classification is a simple and quite effective method. But it has some drawbacks in that it suffers from both so called the curse of dimensionality and computational burden when there are lots of examples. A lot of features in high dimensional data degrade the performance of KNN due to the noisy and redundant variables. Hence some kinds of feature selection mechanism is essential to KNN. Random KNN simplifies the feature selection problem by utilizing an ensemble of KNNs constructed with randomly selected subsets of features. Two kinds of enhancements of the random KNN methodology are proposed in this paper. One is to add an instance selection procedure and the other is to determine the weights by training another combining model instead of simple voting in the random KNN. These two additional procedures applied to the random KNN can improve the classification performance of the original random KNN, which is verified by some data analyses with a real data set.
목차
등록된 정보가 없습니다.
참고문헌 (26)
참고문헌 신청
Abu-Alfeilat, H. A. / 2019 / Effects of distance measure choice on k-nearest neighbor classifier performance : a review / Big Data 7(4):221~248
Bao, Y. / 2004 / Combining multiple k-nearest neighbor classifiers using different distance functions / Proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning, Lecture notes in Computer Science 3177:634~641
Baoli, L. / 2003 / An improved k-nearest neighbor algorithm for text categorization / Proceedings of the 20th International Conference on Computer Processing of Oriental Languages
Bay, S. D. / 1998 / Combining nearest neighbor classifiers through multiple feature subsets / Proceedings of the 15th International Conference on Machine Learning :37~45
Domeniconi, C. / 2004 / Nearest neighbor ensemble / Proceedings of the 17th International Conference on Pattern Recognition 1:228~231
Bao, Y. / 2004 / Combining multiple k-nearest neighbor classifiers using different distance functions / Proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning, Lecture notes in Computer Science 3177:634~641
Baoli, L. / 2003 / An improved k-nearest neighbor algorithm for text categorization / Proceedings of the 20th International Conference on Computer Processing of Oriental Languages
Bay, S. D. / 1998 / Combining nearest neighbor classifiers through multiple feature subsets / Proceedings of the 15th International Conference on Machine Learning :37~45
Domeniconi, C. / 2004 / Nearest neighbor ensemble / Proceedings of the 17th International Conference on Pattern Recognition 1:228~231
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