Data discretization unification

Knowledge and Information Systems

Volumn 19, Issue 1, 2009, Pages 1-29

  Jin, Ruoming ^a   Breitbart, Yuri ^a   Muoh, Chibuike ^a  

^a KENT STATE UNIVERSITY   (United States)

Author keywords

Chi square test; Discretization; Entropy; G2 test; Gini index; MDLP

Indexed keywords

DISCRETE EVENT SIMULATION; ENTROPY; STATISTICAL TESTS; VOLUME MEASUREMENT;

CHI-SQUARE TESTS; DISCRETIZATIONS; DYNAMIC PROGRAMMING ALGORITHM; GENERALIZED ENTROPIES; GINI INDEX; MDLP; MINIMAL DESCRIPTION LENGTH; THEORETICAL COMPLEXITY;

DYNAMIC PROGRAMMING;

EID: 63649087068     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0142-6     Document Type: Article

References (38)

1. Agresti A (1990) Categorical data analysis. Wiley, New York
2. Auer P, Holte R, Maass W (1995) Theory and applications of agnostic pac-learning with small decision trees. In: Machine learning: proceedings of the twelth international conference. Morgan Kaufmann
3. Bay SD (2001) Multivariate discretization for set mining. Knowl Inf Syst 3(4):491-512
4. Breiman L, Friedman J, Olshen R, Stone C (1998) Classification and regression trees. CRC Press
5. Boulle M (2004) Khiops: A statistical discretization method of continuous attributes. Mach Learn 55:53-69
6. Boulle M (2006) MODL: A Bayes optimal discretization method for continuous attributes. Mach Learn 65(1):131-165
7. Casella G, Berger RL (2001) Statistical inference, 2nd edn. Duxbury Press
8. Catlett J (1991) On changing continuous attributes into ordered discrete attributes. In: Proceedings of European working session on learning, pp 164-178
9. Ching JY, Wong AKC, Chan KCC (1995) Class-dependent discretization for inductive learning from continuous and mixed-mode data. IEEE Trans Pattern Anal Mach Intell 17(7):641-651
10. Chmielewski MR, Grzymala-Busse JW (1996) Global discretization of continuous attributes as preprocessing for machine learning. Int J Approx Reason 15
11. Cover TM, Thomas JA (2006) Elements of information thoery, 2nd edn. Wiley, New York
12. Dougherty J, Kohavi R, Sahavi M (1995) Supervised and unsupervised discretization of continuous attributes. In: Proceedings of the 12th international conference on machine learning, pp 194-202
13. Elomaa T, Rousu J (2003) Necessary and sufficient pre-processing in numerical range discretization. Knowl Inf Syst 5(2):162-182
14. Elomaa T, Rousu J (2004) Efficient multisplitting revisited: optima-preserving elimination of partition candidates. Data Mining Knowl Discovery 8:97-126
15. Fayyad UM, Irani KB (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th joint conference on artificial intelligence, pp 1022-1029
16. Girosi F, Jones M, Poggio T (1995) Regularization theory and neural networks architectures. Neural Comput 7(2):219-269
17. Hand D, Mannila H, Smyth P (2001) Principles of data mining. MIT Press
18. Hansen MH, Yu B (2001) Model selection and the principle of minimum description length. J Am Statist Assci 96:454
19. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, Heidelberg
20. Holte RC (1993) Very simple calssification rules perform well on most commonly used datasets. Mach Learn 11:63-90
21. Johnson N, Kotz S, Balakrishnan N (1994) Continuous univariate distributions, 2nd edn. Wiley, New York
22. Jin R, Breitbart Y (2007) Data discretization unification. Technical Report, Department of Computer Science, Kent State University. http://www.cs.kent.edu/research/techrpts.html
23. Kerber R (1992) ChiMerge: Discretization of numeric attributes. In: National conference on artificial intelligence
24. Kurgan LA, Cios KJ (2004) CAIM discretization algorithm. IEEE Trans Knowl Data Eng 16(2):145-153
25. Kohavi R, Sahami M (1996) Error-based and entropy-based discretization of continuous features. In: Proceedings of the second international conference on knowledge discovery and data mining. Menlo Park. AAAI Press, pp 114-119
26. Liu H, Hussain F, Tan CL, Dash M (2002) Discretization: An enabling technique. Data Mining Knowl Discovery 6:393-423
27. Liu H, Setiono R (1995) Chi2: Feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE int'l conference on tools with artificial intelligence
28. Liu X, Wang H (2005) A discretization algorithm based on a heterogeneity criterion. IEEE Trans Knowl Data Eng 17(9):1166-1173
29. Mussard S, Seyte F, Terraza M (2003) Decomposition of Gini and the generalized entropy inequality measures. Econ Bull 4(7):1-6
30. Pfahringer B (1995) Supervised and unsupervised discretization of continuous features. In: Proceedings of 12th international conference on machine learning, pp 456-463
31. Rissanen J (1978) Modeling by shortest data description. Automatica 14:465-471
32. Simovici DA, Jaroszewicz S (2002) An axiomatization of partition entropy. IEEE Trans Inf Theory 48(7):2138-2142
33. Wallace DL (1959) Bounds on normal approximations to Student's and the Chi-square distributions. Ann Mathe Stat 30(4):1121-1130
34. Wallace DL (1960) Correction to "Bounds on Normal Approximations to Student's and the Chi-Square Distributions". Ann Math Statist 31(3):810
35. Wong AKC, Chiu DKY (1987) Synthesizing statistical knowledge from incomplete mixed-mode data. IEEE Trans Pattern Anal Mach Intell 9(6):796-805
36. Yang Y, Webb GI (2003) Weighted proportional k-interval discretization for naive-Bayes classifiers. In: Advances in knowledge discovery and data mining: 7th Pacific-Asia Conference, PAKDD, pp 501-512
37. UCI Machine Learning Repository (2007) http://www.ics.uci.edu/mlearn/ ML.Repository.html
38. Weka 3 (2007) Data mining software in Java. http://www.cs.waikato.ac.nz/ ml/weka