Advanced SearchSearch Tips
Rule-Based Classification Analysis Using Entropy Distribution
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Rule-Based Classification Analysis Using Entropy Distribution
Lee, Jung-Jin; Park, Hae-Ki;
  PDF(new window)
Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.
Rule-based classification analysis;maximum entropy distribution;iterative proportional fitting algorithm;
 Cited by
이정진 (2005). Discriminant analysis of binary data with multinomial distribution by using the iterative cross entropy minimization, <한국통계학회논문집>, 12, 125-137. crossref(new window)

이정진, 김수관 (2002). Classification analysis in information retrieval by using Gauss patterns, <한국통계학회논문집>, 9, 1-11. crossref(new window)

이정진, 황준 (2003). Discriminant analysis of binary data by using the maximum entropy distribution, <한국통계학회논문집>, 10, 909-917. crossref(new window)

Asparoukhov, O. K. and Krzanowski, W. J. (2001). A comparison of discriminant procedures for binary variables, Computational Statistics and Data Analysis, 38, 139-160. crossref(new window)

Cramer, E. (2000). Probability measures with given marginals and conditionals: I-projections and conditional iterative proportional fitting, Statistics & Decisions, 18, 311-329.

Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification, Wiley, New York.

Han, J. and Kamber, M. (2000). Data Mining Concepts and Technique, Elsevier.

Ireland, C. T. and Kullback, S. (1968). Contingency tables with given marginals, Biometrika, 55, 179-188. crossref(new window)

Kantor, P. B. and Lee, J. J. (1998). Testing the maximum entropy principle for information retrieval, Journal of American Society for Information Science, 49, 557-566. crossref(new window)

Lachenbruch (1981). Discriminant Analysis, Prentice Hall.

Liu, B., Hsu, W. and Ma, Y. (1998). Integrating classification and association rule mining, Proceeding 1998 International Conference Knowledge Discovery and Data Mining, 80-86, New York, August 1998.

Ruschendorf, L. (1995) Convergence of the iterative proportional fitting procedure, The Annals of Statistics, 23, 1160-1174. crossref(new window)