Learning to use a learned model: A two-stage approach to classification

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2006, Pages 33-42

  Antonie, Maria Luiza ^a   Zaïane, Osmar R ^a   Holte, Robert C ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION MODEL; RIPPER; RULE-BASED CLASSIFIERS; TEXT CLASSIFICATION;

DATABASE SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; SYSTEMS ANALYSIS;

CLASSIFICATION (OF INFORMATION);

EID: 52149089080     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2006.97     Document Type: Conference Paper

References (29)

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of SIGMOD, pages 207-216, 1993.
2. M.-L. Antonie and O. R. Zaïane. Text document categorization by term association. In Proc. of ICDM, pages 19-26, 2002.
3. C. Apte, F. Damerau, and S. Weiss. Automated learning of decision rules for text categorization. ACM Transactions on Information Systems, 12(3):233-251, 1994.
4. R. Bayardo. Brute-force mining of high-confidence classification rules. In Proc. of SIGKDD, pages 123-126, 1997.
5. C. Blake and C. Merz. UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
6. C. Borgelt. Backpropagation software, http://fuzzy.cs.unimagdeburg.de/ ~borgelt/mlp.html.
7. L. Breiman. Bagging predictors. Machine Learning, 24:123-140, 1996.
8. F. Coenen and P. H. Leng. An evaluation of approaches to classification rule selection. In Proc. of ICDM, pages 359-362, 2004.
9. W. Cohen. Fast effective rule induction. In Proc. of ICML, pages 115-123, 1995.
10. W. Cohen and H. Hirsch. Joins that generalize: text classification using Whirl. In Proc. of SIGKDD, pages 169-173, 1998.
11. J. Demsar. Statistical comparisons of classifiers over multiple datasets. Journal of Machine Learning Research, 7(1): 1-30, 2006.
12. J. Furnkranz and G. Widmer. Incremental reduced error pruning. In Proc. of ICML, pages 70-77, 1994.
13. B. Goethals and M. Zaki, editors. FIMI'03: Workshop on Frequent Itemset Mining Implementations, volume 90 of CEUR Workshop Proceedings series, 2003.
14. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proc. of SIGMOD, pages 1-12, 2000.
15. D. A. Hull. Improving text retrieval for the routing problem using latent semantic indexing. In Proc. of SIGIR, pages 282-289, 1994.
16. T. Joachims. Text categorization with support vector machines: learning with many relevant features. In Proc. of ECML, pages 137-142, 1998.
17. D. Lewis. Naïve (Bayes) at forty: The independence assumption in information retrieval. In Proc. of ECML, pages 4-15, 1998.
18. W. Li, J. Han, and J. Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. In Proc. of ICDM, pages 369-376, 2001.
19. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Proc. of SIGKDD, pages 80-86, 1998.
20. M. Maron. Automatic indexing: An experimental inquiry. Journal of the Association for Computing Machinery, 8(3):404-417, 1961.
21. J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
22. R. Rak, W. Stach, and O. R. Zaïane and M.-L. Antonie. Considering re-occurring features in associative classifiers. In Proc. of PAKDD, pages 240-248, 2005.
23. The Reuters-21578 text categorization test collection. http://www.research.att.com/~lewis/reuters21578.html.
24. R. E. Schapire. Theoretical views of boosting. In Proc. of European Conference on Computational Learning Theory (EuroCOLT), pages 1-10, 1999.
25. I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
26. D. H. Wolpert. Stacked generalization. Neural Networks, 5:241-259, 1992.
27. Y. Yang. An evaluation of statistical approaches to text categorization. Information Retrieval, 1:69-90, 1999.
28. X. Yin and J. Han. CPAR: Classification based on predictive association rules. In Proc. of SDM, 2003.
29. O. R. Zaïane and M.-L. Antonie. On pruning and tuning rules for associative classifiers. In Proc. of Int'l Conf. on Knowledge-Based Intelligence Information & Engineering Systems (KES'05), pages 966-973, 2005.