Improving associative classification by incorporating novel interestingness measures

Expert Systems with Applications

Volumn 31, Issue 1, 2006, Pages 184-192

  Lan, Yu ^a   Janssens, Davy ^b   Chen, Guoqing ^a   Wets, Geert ^b  

^a Tsinghua University   (China)

^b HASSELT UNIVERSITY   (Belgium)

Author keywords

Associative classification; Credit scoring; Dilated chi square; Intensity of implication

Indexed keywords

ALGORITHMS; BENCHMARKING; DECISION THEORY;

ASSOCIATIVE CLASSIFICATION; CREDIT SCORING; DILATED CHI-SQUARE; INTENSITY OF IMPLICATION;

CLASSIFICATION (OF INFORMATION);

EID: 33644775714     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2005.09.015     Document Type: Article

References (23)

1. R. Agrawal, and R. Srikant Fast algorithm for mining association rules Proceedings of 20th international conference on very large data bases, Santiago, Chile 1994 (pp. 487-499)
2. R. Agrawal, T. Imielinski, and A. Swami Mining association rules between sets of items in large databases. Proceedings of the 93' ACM SIGMOID conference on management of data, Washington, DC 1993 (pp. 207-216)
3. E. Altman Financial ratios, discriminant analysis and the prediction of corporate bancruptcy The Journal of Finance 23 1968 589 609
4. C.L. Blake, and C.J. Merz UCI repository of machine learning databases 1998 University of California, Department of Information and Computor Science Irvine, CA. http://www.ics.uci.edu/∼mlearn/mlrepository.htm
5. T.G. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 7 1998 1895 1924
6. G. Dong, X. zhang, L. Wong, and J. Li CAEP:Classification by aggregating emerging patterns. Proceedings of the second international conference on discovery science. Lecture notes in artificial intelligence 1999 Vol. 1721 1999 Springer Tokyo, Japan
7. U.M. Fayyad, and K.B. Irani Multi-interval discretization of continuous valued attributes for classification learning. Proceedings of the 13th international joint conference on artificial intelligence, Chambery, France 1993
8. G. Gras, and A. Lahrer L'implication statistique: une nouvelle methode d'analysis de donnees. Mathematiques, Informatique et Sciences Humaines No. 20 1993
9. S. Guillaume, F. Guillet, and J. Philippe Improving the discovery of association rules with intensity of implication Principles of data mining and knowledge discovery. Lecture notes in artificial intelligence Vol. 1510 1998 (pp. 318-327)
10. M.H. Guo Credit rating 2003 China Renmin University Press Beijing
11. Janssens, D., Wets, G., Brijs, T., & Vanhoof, K., (2005). Adapting the CBA-algorithm by means of intensity of implication. Expert systems with Application, 28, 105-117.
12. J.W. Kim Expert systems for bond rating: A comparative analysis of statistical, rule-based and neural network systems Expert Systems 10 1993 167 171
13. B. Liu, W. Hsu, and Y. Ma Integrating classification and association rule mining. Proceedings of the fourth international conference on discovery and data mining, New York, US 1998 (pp. 80-86)
14. W. Liu, J. Han, and J. Pei CMAR: Accurate and efficient classification based on multiple class-association rules. Proceedings of ICDM'01, San Jose, CA 2001 (pp. 369-376)
15. F. Mills Statistical methods 1955 Pitman London
16. J. Moody, and J. Utans Architecture selection strategies for neural networks application to corporate bond ratings A. Refenes Neural networks in the capital markets 1995 Wiley Chichester 277 300
17. G.E. Pinches, and K.A. Mingo A multivariate analysis of industrial bond ratings Journal of Finance 30 1 1973 1 18
18. J.R. Quinlan C4.5 programs for machine learning 1993 Morgan Kaufmann San Mateo, CA
19. E. Suzuki, and Y. Kodratoff Discovery of surprising exception rules based on intensity of implication. Proceedings of PKDD'98 1998 Springer Berlin (pp. 10-18)
20. K. Wang, and S. Zhou Growing decision trees on support-less association rules. Proceedings of KDD'00, Boston, MA 2000
21. I.H. Witten, and E. Frank Data mining: Practical machine learning tools and techniques with Java implementations 2000 Morgan Kaufmann San Francisco, CA
22. X. Yin, and J. Han CPAR: Classification based on predictive association rules. Proceedings of 2003 SIAM international conference on data mining, San Fransisco, CA 2003 (pp. 331-335)
23. L. Yu, G. Chen, D. Janssens, and G. Wets Dilated Chi-square: A novel interestingness measure to build accurate and compact decision list. Proceedings of the international conference on intelligent information processing, Beijing, China 2004 (pp. 233-237)