A Hellinger-based discretization method for numeric attributes in classification learning

Knowledge-Based Systems

Volumn 20, Issue 4, 2007, Pages 419-425

  Lee, Chang Hwan ^a  

^a Dongguk University   (South Korea)

Author keywords

Data mining; Discretization; Knowledge discovery; Machine learning

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; DATA MINING; INFORMATION THEORY; SENSITIVITY ANALYSIS;

CLASSIFICATION ALGORITHMS; CONTEXT SENSITIVE; DISCRETIZATION; HELLINGER DIVERGENCE;

LEARNING SYSTEMS;

EID: 34047243825     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2006.06.005     Document Type: Article

References (16)

1. Beran R.J. Minimum Hellinger distances for parametric models. The Annals of Statistics 5 (1977) 445-463
2. Kadota T., and Shepp L.A. On the best finite set of linear observables for discriminating two Gaussian signals. IEEE Transactions on Information Theory 13 (1967) 278-284
3. Breiman L., Fiedman J.H., Olshen R.A., and Stone C.J. Classification and Regression Trees (1984), Wadsworth, Belmont, CA
4. J. Catlett, On changing continuous attributes into ordered discrete attributes, in: European Working Session on Learning, 1991.
5. Clark P., and Niblett T. The CN2 induction algorithm. Machine Learning 3 (1989) 261-283
6. J. Dougherty, R. Kohavi, M. Sahami, Supervised and unsupervised discretization of continuous features, in: 12th International Conference on Machine Learning, 1995.
7. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: 13th International Joint Conference of Artificial Intelligence, 1993, pp. 1022-1027.
8. Ying Z. Minimum Hellinger distance estimation for censored data. The Annals of Statistics 20 3 (1992) 1361-1390
9. Kononenko I. Inductive and bayesian learning in medical diagnosis. Applied Artificial Intelligence 7 (1993) 317-337
10. Kullback S. Information Theory and Statistics (1968), Dover Publications, New York
11. P.M. Murphy, D.W. Aha, UCI repository of machine learning databases, , 1996.
12. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann Publisher, Los Altos, CA
13. A. Renyi, On measures of entropy and information, in: Proceedings of Fourth Berkeley Symposium, vol. 1, 1961, pp. 547-561.
14. Tay F.E.H., and Shen L. A modified Chi2 algorithm for discretization. IEEE Transactions on Knowledge and Data Engineering 14 3 (2002)
15. D. Ventura, T. Martinez, BRACE: a paradigm for the discretization of analog data, in: 7th Florida Artificial Intelligence Research Symposium, 1994.
16. Weiss S.M., Galen R.S., and Tapepalli P.V. Maximizing the predictive value of production rules. Artificial Intelligence 45 (1990) 47-71