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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.16 No.2
- 발행연도
- 2016.6
- 수록면
- 81 - 86 (6page)
이용수
초록· 키워드
오류제보하기
Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.
목차
Abstract
1. Introduction
2. Existing Term Weighting Schemes
3. Combining Base Term Weight Vectors
4. Experiments
5. Conclusion
References
참고문헌 (12)
참고문헌 신청
[학술대회논문] - A. L. Maas / 2011 / Learning word vectors for sentiment analysis / Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
[학술대회논문] - M. Sahami / 1998 / A bayesian approach to filtering junk e-mail / Proceedings of the 21st national conference on Artificial intelligence (AAAI)
[학술대회논문] - Upasana / 2010 / A survey on text classification techniques for e-mail filtering / International Conference on Machine Learning and Computing
[학술대회논문] - T. Joachims / 1998 / Text categorization with suport vector machines:Learning with many relevant features / European Conference on Machine Learning
[학술대회논문] - A. Chy / 2014 / Bangla news classifi-cation using naive Bayes classifier / International Conference on Computer and Information Technology
[학술대회논문] - M. Sahami / 1998 / A bayesian approach to filtering junk e-mail / Proceedings of the 21st national conference on Artificial intelligence (AAAI)
[학술대회논문] - Upasana / 2010 / A survey on text classification techniques for e-mail filtering / International Conference on Machine Learning and Computing
[학술대회논문] - T. Joachims / 1998 / Text categorization with suport vector machines:Learning with many relevant features / European Conference on Machine Learning
[학술대회논문] - A. Chy / 2014 / Bangla news classifi-cation using naive Bayes classifier / International Conference on Computer and Information Technology
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