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Forward supervised discretization for multivariate with categorical responses
1. | School of Mathematics and Information Science, Guangzhou University, Guangzhou, Guangdong 510006, China, China |
References:
[1] |
M. Boulle, Khiops: A statistical discretization method of continuous attributes, Machine Learning, 55 (2004), 53-69.
doi: 10.1023/B:MACH.0000019804.29836.05. |
[2] |
J. Catlett, On changing continuous attributes into ordered discrete attributes, In: Machine LearningEWSL-91, 482 (1991), 164-178.
doi: 10.1007/BFb0017012. |
[3] |
D. Chiu, B. Cheung and A. Wong, Information synthesis based on hierarchical maximum entropy discretization, Journal of Experimental and Theoretical Artificial Intelligence, 2 (1989), 117-129.
doi: 10.1080/09528139008953718. |
[4] |
M. Chmielewski and J. Grzymala-Busse, Global discretization of continuous attributes as preprocessing for machine learning, International Journal of Approximate Reasoning, 15 (1996), 319-331.
doi: 10.1016/S0888-613X(96)00074-6. |
[5] |
J. Dougherty, R. Kohavi and M. Sahami, Supervised and unsupervised discretization of continuous features, In Machine learning-International Workshop. Morgan Kaufmann Publishers, 2 (1995), 194-202.
doi: 10.1016/B978-1-55860-377-6.50032-3. |
[6] |
U. Fayyad and K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, Proceedings of the International Joint Conference on Uncertainty in AI, 2 (1993), 1022-1027. |
[7] |
G. Gan, C. Ma and J. Wu, Data clustering: Theory, algorithms, and applications(ASA-SIAM series on statistics and applied probability), Society for Industrial and Applied Mathematics, 20 (2007), xxii+466 pp.
doi: 10.1137/1.9780898718348. |
[8] |
L. Goodman and W. Kruskal, Measures of association for cross classifications, Journal of the American Statistical Association, 49 (1954), 732-764. |
[9] |
I. Guyon and A. Elisseeff, An Introduction to Variable and Feature Selection, Applied Physics Letters, 3 (2002), 1157-1182. |
[10] |
R. Holte, Very sim1ple classification rules perform well on most commonly used datasets, Machine Learning, 11 (1993), 63-90. |
[11] |
W. Huang and Y. Pan, On balalncing between optimal and proportional predictions, Big Data and Information Analytics, 1 (2016), 129-137. |
[12] |
W. Huang, Y. Pan and J. Wu, Supervised discretization with $GK-\tau$, In Procedia Computer Science, 17 (2013), 114-120. |
[13] |
W. Huang, Y. Pan and J. Wu, Supervised discretization with $GK-\lambda$, Procedia Computer Science, 30 (2014), 75-80. |
[14] |
W. Huang, Y. Shi and X. Wang, A nomminal association matrix with feature selection for categorical data, Communications in Statistics - Theory and Methods, to appear. |
[15] |
R. Kerber, Chimerge: Discretization of numeric attributes, In Proceedings of the tenth national conference on Artificial intelligence.AAAI Press, 1994, 123-128. |
[16] |
S. Kotsiantis and D. Kanellopoulos, Discretization techniques: A recent survey, GESTS International Transactions on Computer Science and Engineering, 32 (2006), 47-58. |
[17] |
H. Liu and R. Setiono, Chi2: Feature selection and discretization of numeric attributes, In: Proceedings of the Seventh International Conference on Tools with Artificial Intelligence, 55 (1995), 388-391. |
[18] |
C. Lloyd, Statistical Analysis with Missing Data, John Wiley & Sons, Inc. 1987, New York, NY, USA. |
[19] |
J. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1 (1967), 281-297. |
[20] |
D. Olson and Y. Shi, Introduction to business data mining, Knowledge and information systems, 2007, McGraw-Hill/Irwin. |
[21] |
I. Rish, An empirical study of the naive bayes classifier, IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001, 41-46. |
[22] |
S. Safavian and D. Landgrebe, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man and Cybernetics, 21 (1991), 660-674.
doi: 10.1109/21.97458. |
[23] | |
[24] |
K. Ting, Discretization of Continuous-Valued Attributes and Instance-Based Learning, Basser Department of Computer Science,University of Sydney, 1994. |
show all references
References:
[1] |
M. Boulle, Khiops: A statistical discretization method of continuous attributes, Machine Learning, 55 (2004), 53-69.
doi: 10.1023/B:MACH.0000019804.29836.05. |
[2] |
J. Catlett, On changing continuous attributes into ordered discrete attributes, In: Machine LearningEWSL-91, 482 (1991), 164-178.
doi: 10.1007/BFb0017012. |
[3] |
D. Chiu, B. Cheung and A. Wong, Information synthesis based on hierarchical maximum entropy discretization, Journal of Experimental and Theoretical Artificial Intelligence, 2 (1989), 117-129.
doi: 10.1080/09528139008953718. |
[4] |
M. Chmielewski and J. Grzymala-Busse, Global discretization of continuous attributes as preprocessing for machine learning, International Journal of Approximate Reasoning, 15 (1996), 319-331.
doi: 10.1016/S0888-613X(96)00074-6. |
[5] |
J. Dougherty, R. Kohavi and M. Sahami, Supervised and unsupervised discretization of continuous features, In Machine learning-International Workshop. Morgan Kaufmann Publishers, 2 (1995), 194-202.
doi: 10.1016/B978-1-55860-377-6.50032-3. |
[6] |
U. Fayyad and K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, Proceedings of the International Joint Conference on Uncertainty in AI, 2 (1993), 1022-1027. |
[7] |
G. Gan, C. Ma and J. Wu, Data clustering: Theory, algorithms, and applications(ASA-SIAM series on statistics and applied probability), Society for Industrial and Applied Mathematics, 20 (2007), xxii+466 pp.
doi: 10.1137/1.9780898718348. |
[8] |
L. Goodman and W. Kruskal, Measures of association for cross classifications, Journal of the American Statistical Association, 49 (1954), 732-764. |
[9] |
I. Guyon and A. Elisseeff, An Introduction to Variable and Feature Selection, Applied Physics Letters, 3 (2002), 1157-1182. |
[10] |
R. Holte, Very sim1ple classification rules perform well on most commonly used datasets, Machine Learning, 11 (1993), 63-90. |
[11] |
W. Huang and Y. Pan, On balalncing between optimal and proportional predictions, Big Data and Information Analytics, 1 (2016), 129-137. |
[12] |
W. Huang, Y. Pan and J. Wu, Supervised discretization with $GK-\tau$, In Procedia Computer Science, 17 (2013), 114-120. |
[13] |
W. Huang, Y. Pan and J. Wu, Supervised discretization with $GK-\lambda$, Procedia Computer Science, 30 (2014), 75-80. |
[14] |
W. Huang, Y. Shi and X. Wang, A nomminal association matrix with feature selection for categorical data, Communications in Statistics - Theory and Methods, to appear. |
[15] |
R. Kerber, Chimerge: Discretization of numeric attributes, In Proceedings of the tenth national conference on Artificial intelligence.AAAI Press, 1994, 123-128. |
[16] |
S. Kotsiantis and D. Kanellopoulos, Discretization techniques: A recent survey, GESTS International Transactions on Computer Science and Engineering, 32 (2006), 47-58. |
[17] |
H. Liu and R. Setiono, Chi2: Feature selection and discretization of numeric attributes, In: Proceedings of the Seventh International Conference on Tools with Artificial Intelligence, 55 (1995), 388-391. |
[18] |
C. Lloyd, Statistical Analysis with Missing Data, John Wiley & Sons, Inc. 1987, New York, NY, USA. |
[19] |
J. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1 (1967), 281-297. |
[20] |
D. Olson and Y. Shi, Introduction to business data mining, Knowledge and information systems, 2007, McGraw-Hill/Irwin. |
[21] |
I. Rish, An empirical study of the naive bayes classifier, IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001, 41-46. |
[22] |
S. Safavian and D. Landgrebe, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man and Cybernetics, 21 (1991), 660-674.
doi: 10.1109/21.97458. |
[23] | |
[24] |
K. Ting, Discretization of Continuous-Valued Attributes and Instance-Based Learning, Basser Department of Computer Science,University of Sydney, 1994. |
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