Feature selection using association word mining for classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2113, Issue , 2001, Pages 211-220

  Ko, Su Jeong ^a   Lee, Jung Hyun ^a  

^a Inha University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; EXPERT SYSTEMS; FEATURE EXTRACTION; INFORMATION RETRIEVAL SYSTEMS; LEARNING ALGORITHMS;

APRIORI ALGORITHMS; DOCUMENT CLASSIFICATION; DOCUMENT FREQUENCY; DOCUMENT REPRESENTATION; FEATURE SELECTION METHODS; HIGH-DIMENSIONAL FEATURE SPACE; INFORMATION GAIN; NAIVE BAYES CLASSIFIERS;

CLASSIFICATION (OF INFORMATION);

EID: 84887440031     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44759-8_22     Document Type: Conference Paper

References (19)

1. R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules”, Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994.
2. R. Agrawal and T. Imielinski and A. Swami, “Mining association rules between sets of items in large databases”, In Proceedings of the 1993 ACM SIGMOD Conference, Washington DC, USA, 1993.
3. W. W. Cohen and Y. Singer, “Context sensitive learning methods for text categorization”, Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 307-315, 1996.
4. V. Hatzivassiloglou and K. McKeown, “Towards the automatic identification of adjectival scales: Clustering adjectives according to meaning”, Proceedings of the 31st Annual Meeting of the ACL, pp. 172-182, 1993.
5. D. D. Lewis, Representation and Learning in Information Retrieval, PhD thesis(Technical Report pp. 91-93, Computer Science Dept., Univ. of Massachussetts at Amherst, 1992.
6. D. D. Lewis and M. Ringuette, “Comparison of two Learning algorithms for text categorization”, Proceedings of the Third Annual Symposium on Document Analysis and Information Retrieval, 1994.
7. Y. H. Li and A. K. Jain, “Classification of Text Documents”, Computer Journal, Vol. 41, No. 8, pp. 537-546, 1998.
8. T. Michael, Maching Learning, McGraw-Hill, pp. 154-200, 1997.
9. D. Mladenic, “Feature subset selection in text-learning”, Proceedings of the 10th European Conference on Machine Learning, pp. 95-100, 1998.
10. D. Mladenic and M. Grobelnik, “Feature selection for classification based on text hierarchy”, Proceedings of the Workshop on Learning from Text and the Web, 1998.
11. I. Moulinier and G. Raskinis and J. Ganascia, “Text categorization: a symbolic approach”, Proceedings of Fifth Annual Symposium on Document Analysis and Information Retrieval, 1996.
12. M. Pazzani, D. Billsus, Learning and Revising User Profiles: The Identification of Interesting Web Sites, Machine Learning 27, Kluwer Academic Publishers, pp. 313-331, 1997.
13. V. Rijsbergen and C. Joost, Information Retrieval, Butterworths, London-second edition, 1979.
14. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983.
15. E. Wiener and J. O. Pederson and A. S. Weigend, “A neural network approach to topic spotting”, Proceedings of the Fourth Annual Symposium on Document Analysis and Information Retrieval, 1995.
16. P. C. Wong and P. Whitney and J. Thomas, “Visualizing Association Rules for Text Mining”, Proceedings of the 1999 IEEE Symposium on Information Visualization, pp. 120-123, 1999.
17. Y. Yang and C. G. Chute, “An example-based mapping method for text categorization and retrieval”, ACM Transaction on Information Systems, pp. 253-277, 1994.
18. Y. Yang and J. O. Pedersen, “A Comparative Study on Feature Selection in Text Categorization”, Proceedings of the Fourteenth International Conference on Machine Learning, pp. 412-420, 1997.
19. Cognitive Science Laboratory, Princeton University, “WordNet-a Lexical Database for English”, http://www.cogsci.princeton.edu/~wn/.