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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.18 No.2
- 발행연도
- 2019.6
- 수록면
- 245 - 251 (7page)
- DOI
- 10.7232/iems.2019.18.2.245
이용수
초록· 키워드
오류제보하기
Fast Flux Discriminant (FFD) is known as one of the high-performance nonlinear binary classifiers, and it is possible to construct a classification model considering the interaction between variables. In order to take account of the interaction between variables, FFD introduces the histogram-based kernel smoothing using subspaces including variable combinations. However, when creating a subspace, the original FFD should cover all variables including combinations of variables with low interaction. Therefore, the disadvantage is that the calculation amount increases exponentially as the dimension increases. In this study, we calculate the similarity between variables by using KL divergence. Then, among the obtained similarities, divisions are performed for each subspace with similar variables. Through this method, we try to reduce the amount of calculation while maintaining classification accuracy by using only combinations of variables that are likely to take high interaction. Through the simulation experiments with Japanese newspaper articles, the effectiveness of our proposed method is clarified.
목차
ABSTRACT
1. INTRODUCTION
2. PRELIMINARIES
3. PROPOSED METHOD
4. EXPERIMENT
5. CONCLUSIONS AND FUTURE WORK
REFERENCES
참고문헌 (29)
참고문헌 신청
Bishop, C. / 2010 / Pattern Recognition and Machine Learning / Springer
Chen, W. / 2014 / Fast flux discriminant for large-scale sparse nonlinear classificatio / Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data mining :621~630
Cover, T. M. / 1991 / Elements of Information Theory / John Wiley & Sons
Dumais, S. / 1998 / Inductive learning algorithm and representations for text categorization / Proceedings of the Seventh International Conference on Information and Knowledge Management (CIKM ’98) :148~155
Fan, R. E. / 2008 / Liblinear : A library for large linear classification / Journal of Machine Learning Research 9:1871~1874
Chen, W. / 2014 / Fast flux discriminant for large-scale sparse nonlinear classificatio / Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data mining :621~630
Cover, T. M. / 1991 / Elements of Information Theory / John Wiley & Sons
Dumais, S. / 1998 / Inductive learning algorithm and representations for text categorization / Proceedings of the Seventh International Conference on Information and Knowledge Management (CIKM ’98) :148~155
Fan, R. E. / 2008 / Liblinear : A library for large linear classification / Journal of Machine Learning Research 9:1871~1874
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