A Study on the Performance Improvement of Rocchio Classifier with Term Weighting Methods (용어 가중치부여 기법을 이용한 로치오 분류기의 성능 향상에 관한 연구)

Kim, Pan Jun (김판준)

doi:10.3743/KOSIM.2008.25.1.211

A Study on the Performance Improvement of Rocchio Classifier with Term Weighting Methods

Journal of the Korean Society for Information Management
Abbr : JKOSIM
2008, 25(1), pp.211~233
DOI : 10.3743/KOSIM.2008.25.1.211
Publisher : 한국정보관리학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : February 19, 2008
Accepted : March 10, 2008
Published : March 30, 2008

Kim, Pan Jun ¹

¹신라대학교

Accredited

ABSTRACT

This study examines various weighting methods for improving the performance of automatic classification based on Rocchio algorithm on two collections(LISA, Reuters-21578). First, three factors for weighting are identified as document factor, document factor, category factor for each weighting schemes, the performance of each was investigated. Second, the performance of combined weighting methods between the single schemes were examined. As a result, for the single schemes based on each factor, category-factor-based schemes showed the best performance, document set-factor-based schemes the second, and document-factor-based schemes the worst. For the combined weighting schemes, the schemes(idf*cat) which combine document set factor with category factor show better performance than the combined schemes(tf*cat or ltf*cat) which combine document factor with category factor as well as the common schemes(tfidf or ltfidf) that combining document factor with document set factor. However, according to the results of comparing the single weighting schemes with combined weighting schemes in the view of the collections, while category-factor-based schemes(cat only) perform best on LISA, the combined schemes(idf*cat) which combine document set factor with category factor showed best performance on the Reuters-21578. Therefore for the practical application of the weighting methods, it needs careful consideration of the categories in a collection for automatic classification.

KEYWORDS

weighting, Rocchio algorithm, classifier, automatic classification, text categorization, feature selection principles

Citation status

* References for papers published after 2023 are currently being built.

[journal] 김판준 / 2007 / 로치오 알고리즘을 이용한 자동분류에서 용어 가중치 기법 / 문헌정보학논집 / 명지대학교, 문헌정보학회 (9) : 157~185

[journal] 김판준 / 2006 / A Study on automatic assignment of descriptors using machine learning / 정보관리학회지 / 한국정보관리학회 23(1) : 279~299

[journal] 김판준 / 2006 / A Study on the Automatic Descriptor Assignment for Scientific Journal Articles Using Rocchio Algorithm / 정보관리학회지 / 한국정보관리학회 23(3) : 69~90

[journal] 이재윤 / 2005 / Empirical Study on Improving the Performance of Text Categorization Considering the Relationships between Feature Selection Criterea and Weighting Methods / 한국문헌정보학회지 / 한국문헌정보학회 39(2) : 123~146

[confproc] 이재윤 / 2000 / 문헌 자동분류에서 용어가중치 기법에 대한 연구 / 제7회 한국정보관리학회 학술대회 논문집 / 이화여자대학교 : 41~44

[book] 정영미 / 1993 / 정보검색론 / 구미무역(주) 출판부

[confproc] Brank, J. / 2007 / Interaction of feature selection methods and linear classification models / Proceedings of the ICML-02 Workshop on Text Learning, Sydney

[journal] Castillo M. D. / 2004 / A multistrategy approach for digital text categorization from imbalanced docu- ments / ACM SIGKDD Explorations Newsletter: Special Issue on Leaning from Imbalanced Datasets 6(1) : 70~79

[confproc] Debole, Franca / 2003 / Super- vised term weighting for automated text categorization / Proceedings of SAC-03, 18th ACM Symposium on Applied Computing / ACM : 784~788

[confproc] Deng, Zhi-Hong / 2004 / A Comparative study on feature weight in text catego- rization / In Proceedings of The Sixth Asia Pacific Web Conference(APWEB 2004) : 588~597

[journal] Forman G. / 2003 / An extensive empirical study of feature selection metrics for text classification / The Journal of Machine Learning Research 3 : 1289~1305

[book] Geng, L. / 2006 / Choosing the right lens: finding what is interesting in data mining in: Quality Measures in Data Mining : 3~24

[confproc] How, Bong Chih / 2004 / An empirical study of feature selection for text categorization based on term weightage / In Proceedings of the 2004 IEEE/WIC/ACM International Con- ference on Web Intelligence : 592~602

[confproc] Joachims, Thorsten / 1996 / A probabilistic analysis of the rocchio algorithm with TFIDF for text categorization / Pro- ceedings of ICML-97, 14th International Conference on Machine Learning : 143~151

[confproc] Joachims, Thorsten / 1998 / Text categorization with support vector machines: learning with many relevant features / Pro- ceedings of the 10th European Con- ference on Machine Learning : 137~142

[confproc] Lan, Man / 2007 / Proposing a new term weighting scheme for text categori- zation / In 21st National Conference on Artificial Intelligence

[confproc] Liu, Ying / 2007 / Han- dling of imbalanced data in text classification: category-based term weights / Natural Language Pro- cessing and Text Mining : 171~192

[confproc] Papineni, K. / 2001 / Why inverse document frequency? / Proceedings of the North American Association for Computational Linguistics / NAACI : 25~32

[journal] Prabowo, Ruby / 2006 / A comparison of feature selection met- hods for an evloving RSS feed corpus / Information Processing and Manage- ment 42 : 1491~1512

[journal] Robertson S. / 2004 / Understanding inverse document frequency: on theoretical arguments for IDF / Journal of Docu- mentation 60(5) : 503~520

[journal] Robertson, S. E. / 1976 / Relevance weighting of search terms / JASIS 27(3) : 129~146

[confproc] Rogati, M. / 2007 / High-Performing Feature Selection for Text Classification / Proceedings of the eleventh inter- national conference on Information and knowledge management, CIKM '02

[journal] Salton, G. / 1981 / The Measurement of term importance in automatic indexing / JASIS 32(3) : 175~186

[book] Salton, G. / 1983 / Introduc- tion to Modern Information Retrieval / McGraw-Hill

[journal] Sebastiani, Fabrizio / 2002 / Machine learning in automated text categorization / ACM Computing Surveys 34(1) : 1~47

[confproc] Soucy P. / 2007 / Beyond TFIDF weighting for text categori- zation in the vector space model / IJCAI-05 proceedings : 1130~1135

[book] Yang, Y. / 1999 / A re-examination of text categorization methods / Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

[journal] Yang, Y. / 1999 / Evaluation of statistical approaches to text categorization / Information Retrieval 1 : 69~90

[confproc] Yang, Y. / 1997 / A com- parative Study on Feature Selection in Text Categorization / Proceedings of ICML-97, 14th International Con- ference on Machine Learning : 412~420

[journal] Yu, C. T. / 1976 / Precision weighting-an effective automatic in- dexing method / Journal of Association for Computing Machinery 23(1) : 76~88

[journal] Yu, C. T. / 1982 / Term weighting in information re- trieval using the term precision model / Journal of Association for Computing Machinery 29(1) : 152~170

[journal] Zheng Z. / 2004 / Feature selection for text categori- zation on imbalanced data / ACM SIGKDD Explorations Newsletter: Special Issue on Leaning from Imba- lanced Datasets 6(1) : 80~89

KJCKorea
Journal Central

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Study on the Performance Improvement of Rocchio Classifier with Term Weighting Methods

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Study on the Performance Improvement of Rocchio Classifier with Term Weighting Methods

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (8)

REFERENCES (33) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.