Conceptual equivalence for contrast mining in classification learning

Data and Knowledge Engineering

Volumn 67, Issue 3, 2008, Pages 413-429

  Yang, Ying ^a   Wu, Xindong ^b   Zhu, Xingquan ^c  

^a Australian Taxation Office Canberra   (Australia)

^b University of Vermont   (United States)

^c FLORIDA ATLANTIC UNIVERSITY   (United States)

Author keywords

Classification learning; Conceptual equivalence; Contrasting data groups; Knowledge discovery and representation

Indexed keywords

EDUCATION; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION;

ABSTRACT LEVELS; CATEGORICAL DATUM; CLASSIFICATION ALGORITHMS; CLASSIFICATION LEARNING; CLASSIFICATION LEARNINGS; CLASSIFICATION RULES; CONCEPTUAL EQUIVALENCE; CONTRASTING DATA GROUPS; DATA GROUPS; EMPIRICAL EVIDENCES; EXISTING METHODS; KNOWLEDGE DISCOVERY AND REPRESENTATION; NUMERIC DATUM; RULE SETS;

EQUIVALENCE CLASSES;

EID: 54349101200     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2008.07.001     Document Type: Article

References (33)

1. S.D. Bay, M.J. Pazzani, Detecting change in categorical data: Mining contrast sets, in: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 302-306.
2. G.I. Webb, S. Butler, D. Newlands, On detecting differences between groups, in: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 256-265.
3. C.E. Brodley, M.A. Friedl, Identifying and eliminating mislabeled training instances, in: Proceedings of the 13th National Conference on Artificial Intelligence (AAAI), 1996, pp. 799-805.
4. Brodley C.E., and Friedl M.A. Identifying mislabeled training data. Journal of Artificial Intelligence Research 11 (1999) 131-167
5. Gamberger D., Lavrac N., and Dzeroski S. Noise detection and elimination in data preprocessing: experiments in medical domains. Applied Artificial Intelligence 14 (2000) 205-223
6. D. Gamberger, N. Lavrac, C. Groselj, Experiments with noise filtering in a medical domain. In: Proceedings of the 16th International Conference on Machine Learning (ICML), 1999, pp. 143-151.
7. Guyon I., Matic N., and Vapnik V. Discovering Informative Patterns and Data Cleaning (1996), AAAI/MIT Press 181-203
8. S. Verbaeten, Identifying mislabeled training examples in ILP classification problems, in: Proceedings of the 12th Belgian-Dutch Conference on Machine Learning, 2002, pp. 1-8.
9. X. Zhu, X. Wu, Q. Chen, Eliminating class noise in large datasets, in: Proceedings of the 20th International Conference on Machine Learning (ICML), 2003, pp. 920-927.
10. J. Kubica, A. Moore, Probabilistic noise identification and data cleaning, in: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), 2003, pp. 131-138.
11. C.M. Tengm, Correcting noisy data, in: Proceedings of the 16th International Conference on Machine Learning, 1999, pp. 239-248.
12. Y. Yang, X. Wu, X. Zhu, Dealing with predictive-but-unpredictable attributes in noisy data sources, in: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2004, pp. 471-483.
13. G. Hulten, L. Spencer, P. Domingos, Mining time-changing data streams, in: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 97-106.
14. W.N. Street, Y. Kim, A streaming ensemble algorithm (sea) for largescale classification, in: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 377-382.
15. J.Z. Kolter, M.A. Maloof, Dynamic weighted majority: a new ensemble method for tracking concept drift, in: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), 2003, p. 123.
16. H. Wang, W. Fan, P.S. Yu, J. Han, Mining concept drifting data streams using ensemble classifiers, in: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 226-235.
17. Y. Yang, X. Wu, X. Zhu, Combining proactive and reactive predictions for data streams, in: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005, pp. 710-715.
18. Yang Y., Wu X., and Zhu X. Mining in anticipation for concept change: proactive-reactive prediction in data streams. Data Mining and Knowledge Discovery (DMKD) 13 3 (2006) 261-289
19. K. Wang, S. Zhou, C.A. Fu, J.X. Yu, Mining changes of classification by correspondence tracing, in: Proceedings of SIAM International Conference on Data Mining (SDM), 2003, pp. 95-106.
20. S. Tsumoto, S. Hirano, Visualization of rule's similarity using multidimensional scaling, in: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), 2003, pp. 339-346.
21. B. Liu, W. Hsu, Post-analysis of learned rules, in: Proceedings of the 13th National Conference on Artificial Intelligence (AAAI), 1996, pp. 828-834.
22. B. Liu, W. Hsu, Y. Ma, Discovering the set of fundamental rule changes, in: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 335-340.
23. Watson I., and Marir F. Case-based reasoning: a review. The Knowledge Engineering Review (1994)
24. Aamodt A., and Plaza E. Case-based reasoning: foundational issues methodological variations and system approaches. AI Communications 7 1 (1994) 39-59
25. M.M. Richter, Classification and learning of similarity measures, Technical Report SR-92-18, University of Kaiserslautern, Federal Republic of Germany, 1992.
26. K.P. Jantke, Nonstandard concepts of similarity in case-based reasoning, in: Proceedings of the 17th Annual Conference on Information Systems and Data Analysis: Prospects-Foundations-Applications, 1994, pp. 28-43.
27. M. Gu, X. Tong, A. Aamodt, Comparing similarity calculation methods in conversational cbr, in: Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI), 2005, pp. 427-432.
28. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, 1998. .
29. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann Publishers
30. E. Frank, I.H. Witten, Generating accurate rule sets without global optimization, in: Proceedings of the 15th International Conference on Machine Learning (ICML), 1998, pp. 152-160.
31. W. Duch, R. Adamczak, K. Grabczewski, Extraction of logical rules from training data using backpropagation networks, in: Proceedings of the 1st Online Workshop on Soft Computing, 1996, pp. 25-30.
32. W. Duch, R. Adamczak, K. Grabczewski, M. Ishikawa, H. Ueda, Extraction of crisp logical rules using constrained backpropagation networks comparison of two new approaches, in: Proceedings of the European Symposium on Artificial Neural Networks, 1997, pp. 109-114.
33. G. Hulten, L. Spencer, P. Domingos, Mining time-changing data streams, in: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 97-106.