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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.10
- 수록면
- 341 - 349 (9page)
- DOI
- 10.7232/JKIIE.2017.43.5.341
이용수
초록· 키워드
오류제보하기
Mixed data with numeric variables and categorical variables are appearing in many areas such as credit scoring, medical diagnosis and manufacturing products. Most Classification techniques are suitable for each variable type of mixed data. For example, techniques using Euclidean distance are suitable for numerical variables and another techniques using symbolic logic are suitable for categorical variables. In this paper, we propose a hybrid method of classifiers to improve performance of the classification algorithm. Main idea is to deal with the categorical and numerical attributes separately with appropriate techniques. First, a whole data is partitioned into several subsets by applying decision tree only to categorical variables. Next, posterior probability is obtained by applying either k-NN or SVM to numerical variables in each leaf node of decision tree. Six data (Australian credit, German credit, Japan credit, Mammographic mass, churn, bank) of the UCI Machine Learning Repository are used to evaluate performance of the proposed hybrid classifier. Performance of a hybrid k-NN classifier is improved comparing with the k-NN. Performance of a hybrid SVM is slightly better than that of SVM.
목차
1. 서론
2. 연구 배경
3. 하이브리드 기법
4. 하이브리드 기법 실험 결과
5. 결론 및 향후 연구
참고문헌
참고문헌 (34)
참고문헌 신청
[학술대회논문] - Boriah, S. / 2008 / Similarity Measures for Categorical Data : A Comparative Evaluation / Proceedings of the 2008 SIAM International Conference on Data Mining
[학술지(정기간행물)] - Brodley, C. E. / 1995 / Multivariate Decision Trees / Machine Learning 19 (1) : 45 ~ 77
[학술지(정기간행물)] - Burges, C. J. C. / 1998 / A tutorial on support vector machines for pattern recognition / Data Mining and Knowledge Discovery 2 : 121 ~ 167
[학술지(정기간행물)] - Chen, Y. / 2009 / Similarity-based Classification : Concepts and Algorithms / Journal of Machine Learning Research 10 : 747 ~ 776
[단행본] - Cohen, J. / 2013 / Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences / Lawrence Erlbaum Associates Inc
[학술지(정기간행물)] - Brodley, C. E. / 1995 / Multivariate Decision Trees / Machine Learning 19 (1) : 45 ~ 77
[학술지(정기간행물)] - Burges, C. J. C. / 1998 / A tutorial on support vector machines for pattern recognition / Data Mining and Knowledge Discovery 2 : 121 ~ 167
[학술지(정기간행물)] - Chen, Y. / 2009 / Similarity-based Classification : Concepts and Algorithms / Journal of Machine Learning Research 10 : 747 ~ 776
[단행본] - Cohen, J. / 2013 / Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences / Lawrence Erlbaum Associates Inc
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UCI(KEPA) : I410-ECN-0101-2018-530-001328330