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From Association to Classification: Inference Using Weight of Evidence
May/June 2003 (vol. 15 no. 3)
pp. 764-767

Abstract—Association and classification are two important tasks in data mining and knowledge discovery. Intensive studies have been carried out in both areas. But, how to apply discovered event associations to classification is still seldom found in current publications. Trying to bridge this gap, this paper extends our previous paper on significant event association discovery to classification. We propose to use weight of evidence to evaluate the evidence of a significant event association in support of, or against, a certain class membership. Traditional weight of evidence in information theory is extended here to measure the event associations of different orders with respect to a certain class. After the discovery of significant event associations inherent in a data set, it is easy and efficient to apply the weight of evidence measure for classifying an observation according to any attribute. With this approach, we achieve flexible prediction.

[1] D.H. Fisher and K.B. McKusick, “An Empirical Comparison of ID3 and Back-Propagation,” Proc. 11th Int'l Joint Conf. Artificial Intelligence, vol. 1, pp. 788-793, 1989.
[2] A.K.C. Wong and Y. Wang, “High-Order Pattern Discovery from Discrete-Valued Data Sets,” IEEE Trans. Knowledge and Data Eng., pp. 877-893, vol. 9, no. 6, Nov./Dec. 1997.
[3] D.B. Osteyee and I.J. Good, Information, Weight of Evidence, the Singularity between Probability Measures and Signal Detection. Berlin, Germany: Springer-Velag, 1974.
[4] R. Agrawal, T. Imielinski, and A. Swami, Database Mining: A Performance Perspective IEEE Trans. Knowledge and Data Eng., vol. 5, no. 6, Dec. 1993.
[5] J.R. Quinlan,"Induction of decision trees," Machine Learning, vol. 1, pp. 81-106, 1986.
[6] L. Breiman, J.H. Freidman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees. Wadsworth Belmont, 1984.
[7] R. Agrawal, H. Manilla, R. Srikant, H. Toivonen, and A.I. Verkami, “Fast Discovery of Association Rules,” Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, eds., pp. 307-328, 1996.
[8] B. Liu, W. Hsu, and Y. Ma, “Integration Classification and Association Rule Mining,” Proc. Fourth Int'l Conf. Knowledge Discovery and Data Mining (KDD '98), pp. 80-86, Aug. 1998.
[9] J.R. Quinlan, C4.5: Programs for Machine Learning,San Mateo, Calif.: Morgan Kaufman, 1992.
[10] S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Basket: Generalizing Association Rules to Correlations,” Proc. 1997 ACM-SIGMOD Int'l Conf. Management of Data, pp. 265-276, May 1997.
[11] R.J. Bayardo, “Brute-Force Mining of High-Confidence Classification Rules,” Proc. Third Int'l Conf. Knowledge Discovery and Data Mining (KDD '97), pp. 115-118, 1997.
[12] K. Ali, S. Manganaris, and R. Srikant, “Partial Classification Using Association Rules,” Proc. Third Int'l Conf. Knowledge Discovery and Data Mining (KDD '97), pp. 115-118, Aug. 1997.
[13] K.C.C. Chan and A.K.C. Wong,“APACS: a system for automated pattern analysis and classification,” Computational Intelligence, vol. 6, 1990.
[14] P.M. Murph and D.W. Aha, “UCI Repository of Machine Learning Databases,” Dept. of Information and Computer Science, Univ. of California, Irvine, 1991.
[15] J.S. Schlimmer, "Concept Acquisition Through Representational Adjustment," PhD thesis, Dept. of Information and Science, Univ. of California at Irvine, May 1996.
[16] W. Iba, W. James, and P. Langley, “Trading Off Simplicity and Coverage in Incremental Concept Learning,” Proc. Fifth Int'l Conf. Machine Learning, pp. 73-79, 1988.

Index Terms:
Classification, data mining, event association, pattern discovery, weight of evidence.
Yang Wang, Andrew K.C. Wong, "From Association to Classification: Inference Using Weight of Evidence," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 3, pp. 764-767, May-June 2003, doi:10.1109/TKDE.2003.1198405
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