Fuzzy partitioning of continuous attributes through discretization methods to construct fuzzy decision tree classifiers

Information Sciences

Volumn 278, Issue , 2014, Pages 715-735

  Zeinalkhani, Mohsen ^a   Eftekhari, Mahdi ^a  

^a SHAHID BAHONAR UNIVERSITY OF KERMAN   (Iran)

Author keywords

Discretization; Fuzzy decision tree; Fuzzy partitioning; Membership function

Indexed keywords

DISCRETE EVENT SIMULATION; ENTROPY; FORESTRY; MEMBERSHIP FUNCTIONS; TREES (MATHEMATICS); VOLUME MEASUREMENT;

CONTINUOUS ATTRIBUTE; DISCRETIZATION ALGORITHMS; DISCRETIZATION METHOD; DISCRETIZATIONS; FUZZY DECISION TREES; FUZZY PARTITIONING; GENERATION ALGORITHM; NON-PARAMETRIC STATISTICAL TESTS;

ALGORITHMS;

ENTROPY; FORESTRY; MATHEMATICS; MEASUREMENT; TREES; VOLUME;

EID: 84901824809     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.03.087     Document Type: Article

References (59)

1. F. Afsari, M. Eftekhari, E. Eslami, and P.Y. Woo Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm Soft Comput. 17 2013 1673 1686
2. J. Alcalá-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, and F. Herrera KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework J. Multiple-Valued Logic Soft Comput. 17 2011 255 287
3. J. Alcalá-Fdez, L. Sánchez, S. García, M. del Jesus, S. Ventura, J. Garrell, J. Otero, C. Romero, J. Bacardit, V. Rivas, J. Fernández, and F. Herrera KEEL: a software tool to assess evolutionary algorithms for data mining problems Soft Comput. - Fusion Found. Methodol. Appl. 13 2009 307 318
4. S.D. Bay Multivariate discretization for set mining Knowl. Inf. Syst. 3 2001 491 512
5. M. Boulle Khiops: a statistical discretization method of continuous attributes Mach. Learn. 55 2004 53 69
6. M. Boullé MODL: A Bayes optimal discretization method for continuous attributes Mach. Learn. 65 2006 131 165
7. J. Cerquides, R.L. Mantaras, Proposal and empirical comparison of a parallelizable distance-based discretization method, in: 3rd International Conference on Knowledge Discovery and Data Mining (KDD99), NewPort Beach, 1997, pp. 139-142.
8. S.M. Chen, and J.D. Shie Fuzzy classification systems based on fuzzy information gain measures Expert Syst. Appl. 36 2009 4517 4522
9. H.D. Cheng, and J.-R. Chen Automatically determine the membership function based on the maximum entropy principle Inf. Sci. 96 1997 163 182
10. Z. Chi, H. Yan, and T. Pham Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition 1996 World Scientific Publishing Co. Inc.
11. J.Y. Ching, A.K.C. Wong, and K.C.C. Chan Class-dependent discretization for inductive learning from continuous and mixed-mode data IEEE Trans. Pattern Anal. Mach. Intell. 17 1995 641 651
12. M.R. Chmielewski, and J.W. Grzymala-Busse Global discretization of continuous attributes as preprocessing for machine learning Int. J. Approx. Reason. 15 1996 319 331
13. J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
14. J. Dougherty, R. Kohavi, M. Sahami, Supervised and unsupervised discretization of continuous features, in: A. Prieditis, S. Russell (Eds.) Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, 1995, pp. 194-202.
15. D. Dubois, and H. Prade Unfair coins and necessity measures: towards a possibilistic interpretation of histograms Fuzzy Sets Syst. 10 1983 15 20
16. Fayyad, Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of the International Joint Conference on Uncertainty in AI, Chambery, France, 1993, pp. 1022-1027.
17. A. Frank, A. Asuncion, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA, 2010. < http://archive.ics.uci.edu/ml >.
18. S. García, A. Fernández, J. Luengo, and F. Herrera Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power Inf. Sci. 180 2010 2044 2064
19. S. Garcia, and F. Herrera An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons J. Mach. Learn. Res. 9 2008 2677 2694
20. R. Giráldez, J. Aguilar-Ruiz, J.C. Riquelme, Discretización supervisada no paramétrica orientada a la obtención de reglas de decisión, in, 2001, pp. 53-62.
21. L. Gonzalez-Abril, F. Cuberos, F. Velasco, and J. Ortega Ameva: an autonomous discretization algorithm Expert Syst. Appl. 36 2009 5327 5332
22. A. Gupta, K.G. Mehrotra, and C. Mohan A clustering-based discretization for supervised learning Stat. Probabil. Lett. 80 2010 816 824
23. K. Ho, P. Scott, Zeta: a global method for discretization of cotitinuous variables, in: 3rd International Conference on Knowledge Discovery and Data Mining (KDD99), NewPort Beach, USA, 1997, pp. 191-194.
24. R.C. Holte Very simple classification rules perform well on most commonly used datasets Mach. Learn. 11 1993 63 90
25. T.P. Hong, Y.F. Tung, S.L. Wang, Y.L. Wu, and M.T. Wu A multi-level ant-colony mining algorithm for membership functions Inf. Sci. 182 2012 3 14
26. H. Ishibuchi, K. Nozaki, and H. Tanaka Efficient fuzzy partition of pattern space for classification problems Fuzzy Sets Syst. 59 1993 295 304
27. C.Z. Janikow Fuzzy decision trees: issues and methods IEEE Trans. Syst. Man Cybern. B Cybern. 28 1998 1 14
28. J.M. Keller, M.R. Gray, and J.A. Givens A fuzzy K-nearest neighbor algorithm IEEE Trans. Syst. Man, Cybern. B Cybern. 15 1985 580 585
29. R. Kerber Chimerge: discretization of numeric attributes National Conference on Artificial Intelligence 1992 AAAI Press San Jose, CA 123 128
30. S. Kotsiantis, and D. Kanellopoulos Discretization techniques: a recent survey GESTS Int. Trans. Comput. Sci. Eng. 32 2006 47 58
31. L.A. Kurgan, and K.J. Cios CAIM discretization algorithm IEEE Trans. Knowl. Data Eng. 16 2004 145 153
32. C.H. Lee A Hellinger-based discretization method for numeric attributes in classification learning Knowl. Based Syst. 20 2007 419 425
33. H. Liu, F. Hussain, C.L. Tan, and M. Dash Discretization: an enabling technique Data Min. Knowl. Discov. 6 2002 393 423
34. H. Liu, and R. Setiono Feature selection via discretization IEEE Trans. Knowl. Data Eng. 9 1997 642 645
35. X. Liu, and H. Wang A discretization algorithm based on a heterogeneity criterion IEEE Trans. Knowl. Data Eng. 17 2005 1166 1173
36. D. Malchiodi, and W. Pedrycz Learning membership functions for fuzzy sets through modified support vector clustering F. Masulli, G. Pasi, R. Yager, Fuzzy Logic and Applications 2013 Springer International Publishing 52 59
37. S. Medasani, J. Kim, and R. Krishnapuram An overview of membership function generation techniques for pattern recognition Int. J. Approx. Reason. 19 1998 391 417
38. S. Mehta, S. Parthasarathy, and H. Yang Toward unsupervised correlation preserving discretization IEEE Trans. Knowl. Data Eng. 17 2005 1174 1185
39. G. Nieradka, and B. Butkiewicz A method for automatic membership function estimation based on fuzzy measures P. Melin, O. Castillo, L. Aguilar, J. Kacprzyk, W. Pedrycz, Foundations of Fuzzy Logic and Soft Computing 2007 Springer Berlin Heidelberg 451 460
40. W. Pedrycz Fuzzy equalization in the construction of fuzzy sets Fuzzy Sets Syst. 119 2001 329 335
41. W. Pedrycz Granular Computing: Analysis and Design of Intelligent Systems 2013 Taylor & Francis
42. J.R. Quinlan Induction of Decision Trees Mach. Learn. 1 1986 81 106
43. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufman Publishers
44. J. Rissanen Modeling by shortest data description Automatica 14 1978 465 471
45. J. Ross Quinlan, and R.L. Rivest Inferring decision trees using the minimum description length principle Inf. Comput. 80 1989 227 248
46. C.T. Su, and J.H. Hsu An extended Chi2 algorithm for discretization of real value attributes IEEE Trans. Knowl. Data Eng. 17 2005 437 441
47. F.E.H. Tay, and L. Shen A modified Chi2 algorithm for discretization IEEE Trans. Knowl. Data Eng. 14 2002 666 670
48. C.J. Tsai, C.I. Lee, and W.P. Yang A discretization algorithm based on class-attribute contingency coefficient Inf. Sci. 178 2008 714 731
49. M. Umano, H. Okamoto, I. Hatono, H. Tamura, F. Kawachi, S. Umedzu, J. Kinoshita, Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems, in: Proceedings of Third IEEE Conference on Fuzzy Systems, 1994, pp. 2113-2118.
50. X.Z. Wang, D.S. Yeung, and E.C.C. Tsang A comparative study on heuristic algorithms for generating fuzzy decision trees IEEE Trans. Syst. Man Cybern. B 31 2001 215 226
51. Q.X. Wu, D.A. Bell, G. Prasad, and T.M. McGinnity A distribution-index- based discretizer for decision-making with symbolic AI approaches IEEE Trans. Knowl. Data Eng. 19 2007 17 28
52. X. Wu A Bayesian discretizer for real-valued attributes Comput. J. 39 1996 688 691
53. P. Yang, J.S. Li, and Y.X. Huang HDD: a hypercube division-based algorithm for discretisation Int. J. Syst. Sci. 42 2011 557 566
54. Y. Yang, and G.I. Webb Discretization for naive-Bayes learning: managing discretization bias and variance Mach. Learn. 74 2009 39 74
55. Y. Yuan, and M.J. Shaw Induction of fuzzy decision trees Fuzzy Sets Syst. 69 1995 125 139
56. M. Zeinalkhani, M. Eftekhari, A new measure for comparing stopping criteria of fuzzy decision tree, in: International Conference on Computer and Knowledge Engineering, ICCKE2011, Ferdowsi University of Mashhad, Mashhad, Iran, 2011, pp. 120-123.
57. M. Zeinalkhani, and M. Eftekhari Comparing different stopping criteria for fuzzy decision tree induction through IDFID3 Iran. J. Fuzzy Syst. 11 2014 27 48
58. J.h. Zhai Fuzzy decision tree based on fuzzy-rough technique Soft Comput. 15 2011 1087 1096
59. D. Zighed, S. Rabaseda, and R. Rakotomalala FUSINTER: a method for discretization of continuous attributes Int. J. Uncertain. Fuzz. Knowl. Based Syst. 6 1998 307 326