Journal Contributions: Published

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        2008  2007  2006  2005  2004  2003  2002  2001  2000  1999
        1995  1993  1986

2008
	Jr-S. Chen, C.-H. Cheng. Extracting classification rule of software diagnosis using modified MEPA. Expert Systems with Applications 34 (2008) 411-418			bib
	C.-J. Tsai, C.-I. Lee, W.-P. Yang. A discretization algorithm based on Class-Attribute Contingency Coefficient. Information Science 178:3 (2008) 714-731			bib
2007
	A. Kumar, D. Zhang. Hand-Geometry Recognition Using Entropy-Based Discretization. IEEE Transactions on Information Forensics and Security 2:2 (2007) 181-187			bib
	D.-D. Le, S. Satoh. Ent-Boost: Boosting using entropy measures for robust object detection. Pattern Recognition Letters 28 (2007) 1083-1090			bib
	C.-H. Lee. A Hellinger-based discretization method for numerica attributes in classification learning. Knowledge-Based Systems 20 (2007) 419-425			bib
	Q. Wu, D.A. Bell, G. Prasad, T.M. MacGinnity. A Distribution-Index-Based Discretizer for Decision-Making with Symbolic AI Approaches. IEEE Transactions on Knowledge and Data Engineering 19:1 (2007) 17-28			bib
2006
	M.J. Beynon. An Introduction of the Condition Class Space with Continuous Value Discretization and Rough Set Theory. International Journal of Intelligent Systems 21:2 (2006) 173-191			bib
	M. Boulle. MODL: A bayes optimal discretization method for continuous attributes. Machine Learning 65:1 (2006) 131-165			bib
	D. Janssens, T. Brijs, K. Vanhoof, G. Wets. Evaluating the performance of cost-based discretization versus entropy- and error-based discretization. Computers and Operations Research 33:11 (2006) 3107-3123			bib
2005
	F. Divina, E. Marchiori. Handling continuous attributes in an evolutionary inductive learner. IEEE Transactions on Evolutionary Computation 9:1 (2005) 31-43			bib
	X. Liu, H. Wang. A discretization algorithm based on a Heterogenity Criterion. IEEE Transactions on Knowledge and Data Engineering 17:9 (2005) 1166-1173			bib
	C.-T. Su, J.-H. Hsu. An extended Chi2 algorithm for discretization of real value attributes. IEEE Transactions on Knowledge and Data 17:3 (2005) 437-441			bib
2004
	M. Boulle. Khiops: A Statistical Discretization Method of Continuous Attributes. Machine Learning 55:1 (2004) 53-69			bib
	R. Butterworth, D.A. Simovici, G.S. Santos, L. Ohno-Machado. A greedy algorithm for supervised discretization. Journal of Biomedical Informatics 37:4 (2004) 285-292			bib
	T. Elomaa, J. Rousu. Efficient multisplitting revisited: Optima-preserving elimination of partition candidates. Data Mining and Knowledge Discovery 8:2 (2004) 97-126			bib
	L.A. Kurgan, K.J. Cios. CAIM Discretization Algorithm. IEEE Transactions on Knowledge and Data Engineering 16:2 (2004) 145-153			bib
2003
	C.-N. Hsu, H.-J. Huang, T.-T. Wong. Implications of the Dirichlet Assumption for Discretization of Continuous Variables in Naive Bayesian Classifiers. Machine Learning 53 (2003) 235-263			bib
2002
	H. Liu, F. Hussain, C.L. Tan, M. Dash. Discretization: An Enabling Technique. Data Mining and Knowledge Discovery 6:4 (2002) 393-423			bib
2001
	J.W. Grzymala-Busse, J. Stefanowski. Three discretization methods for rule induction. International Journal of Intelligent Systems 16:1 (2001) 29-38			bib
2000
	E.J. Clarke, B.A. Barton. Entropy and MDL discretization of continuous variables for Bayesian belief networks. International Journal of Intelligent Systems 15:1 (2000) 61-92			bib
	K. Kim, I. Han. Genetic algorithms approach to feature discretization in artificial neural netweoks for the prediction of stock price index. Expert Systems and Applications 19 (2000) 125-132			bib
1999
	D. Coppersmith, S.J. Hong, J.R.M. Hosking. Partitioning Nominal Attributes in Decision Trees. Data Mining and Knowledge Discovery 3 (1999) 197-217			bib
1995
	J.Y. Ching, A.K.C. Wong, K.C.C. Chan. Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 17:7 (1995) 641-651			bib
1993
	R.C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 (1993) 63-91			bib
1986
	J.R. Quinlan. Induction of Decision Trees. Machine Learning 1 (1986) 81-106			bib

 Conference Contributions

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        2006  2005  2004  2003  2002  2001  1998  1997  1995  1993
        1992  1991

2006
	A. Ekbal. Improvement of Prediction Accuracy Using Discretization and Voting Classifier. 18th International Conference on Pattern Recognition, 2006 (ICPR 2006). (2006) 695-698			bib
	Y. Kang, S. Wang, X. Liu, H. Lai, H. Wang, B. Miao. Practical Approximation of Optimal Multivariate Discretization. Knowledge Science, Engineering and Management 2006 (KSEM 2006). Lecture Notes in Computer Science 4092, 2006 (2006) 556-562			bib
	J. Lu, Y. Yang, G.I. Webb. Incremental discretization for naïve-bayes classifier. Advanced Data Mining and Applications 2006 (ADMA 2006). Lecture Notes in Computer Science 4093, 2006 (2006) 223-238			bib
	G. Schmidberger, E. Frank. Unsupervised Discretization using Tree-based Density Estimation. Accepted in XVIth European Conference on Machine Learning (ECML05). Porto (Portugal, 2006)		bib
	Q. Wu, J. Cai, G. Prasad, T.M. McGinnity, D. Bell, J. Guan. A Novel Discretizer for Knowledge Discovery Approaches Based on Rough Sets. Rough Sets and Knowledge Technology 2006 (RSKT 2006). Lecture Notes in Computer Science 4062, 2006 (2006) 241-246			bib
2005
	S. Ferrandiz, M. Boull. Multivariate discretization by recursive supervised bipartition of graph. IV International Conference in Machine Learning and Data Mining in Pattern Recognition (MLDM05). Lecture Notes in Computer Science 3587, Springer-Verlag 2005, Leipzig (Germany, 2005) 253-264			bib
	C.-H. Lee. Discretizing continuous attributes using information theory. Computer and Information Sciences 2005 (ISCIS 2005). Lecture Notes in Computer Science 3733, 2005 (2005) 493-502			bib
2004
	J.S. Aguilar-Ruiz, J. Bacardit, F. Divina. Experimental Evaluation of Discretization Schemes for Rule Induction. Genetic and Evolutionary Computation Conference (GECCO04). Lecture Notes in Computer Science 3102, Springer-Verlag 2004, Seattle (Washington USA, 2004) 828-839			bib
	J. Bacardit, J.M. Garrell. Evolving multiple discretizations with adaptive intervals for a Pittsburgh Rule-Based Learning Classifier System. Genetic and Evolutionary Computation Conference (GECCO03). Lecture Notes in Computer Science 2724, Springer-Verlag 2004, Chicago (Illinois USA, 2004) 1818-1831			bib
	J. Bacardit, J.M. Garrell. Analysis and improvements of the Adaptive Discretization Intervals knowledge representation. Genetic and Evolutionary Computation Conference (GECCO04). Lecture Notes in Computer Science 3103, Springer-Verlag 2004, Seattle (Washington USA, 2004) 726-738			bib
	J.W. Grzymala-Busse. Three strategies to rule induction from data with numerical attributes. Transactions on Rough Sets II. Lecture Notes in Computer Science 3135, Springer-Verlag 2004 (2004) 54-62			bib
	H. Steck, T. Jaakkola. Predictive discretization during model selection. XXVI Symposium In Pattern Recognition (DAGM04). Lecture Notes in Computer Science 3175, Springer-Verlag 2004, Tbingen (Germany, 2004) 1-8			bib
2003
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2002
	T. Elomaa, J. Rousu. Fast Minimum Training Error Discretization. XIX International Conference on Machine Learning (ICML02). Sydney (Australia, 2002) 131-138			bib
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	Y. Yang, G.I. Webb. Non-Disjoint Discretization for Naive-Bayes Classifiers. XIX International Conference on Machine Learning (ICML02). Sydney (Australia, 2002) 666-673			bib
2001
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1998
	K. Wang, B. Liu. Concurrent Discretization of Multiple Attributes. 5th Pacific Rim International Conference on Topics in Artificial Intelligence (PRICAI98). Lecture Notes in Computer Science 1531, Springer-Verlag 1998, Singapore (Singapore, 1998) 250-259			bib
1997
	J. Cerquides, R. Lopez. Proposal and Empirical Comparison of a Parallelizable Distance-Based Discretization Method. III International Conference on Knowledge Discovery and Data Mining (KDDM97). Newport Beach (California USA, 1997) 139-142			bib
	K.M. Ho, P.D. Scott. Zeta: A Global Method for Discretization of Continuous Variables. III International Conference on Knowledge Discovery and Data Mining. Newport Beach (California USA, 1997) 191-194			bib
	A.V. Kozlov, D. Koller. Nonuniform Dynamic Discretization in Hybrid Networks. Thirteenth Annual Conference on Uncertainty in AI (UAI97). Providence (Rhode Island USA, 1997) 314-325			bib
1995
	H. Liu, R. Setiono. Chi2: Feature selection and discretization of numeric attributes. VII IEEE International Conference on Tools with Artificial Intelligence (ICTAI95). Whashington DC (USA, 1995) 388-391			bib
1993
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1992
	R. Kerber. ChiMerge: Discretization of Numeric Attributes. X National Conference on Artifical Intelligence American Association for Artificial Intelligence (AAAI92). San Jose (California USA, 1992) 123-128		bib
1991
	J. Catlett. On changing continuous attributes into ordered discrete attributes. European Working Session on Learning (EWSL91). Lecture Notes in Computer Science 482, Springer-Verlag 1991, Porto (Portugal, 1991) 164-178		bib
	C.C. Chan, C. Batur, A. Srinivasan. Determination of quantization intervals in rule based model for dynamic systems. Conference on Systems, Man, and Cybernetics. Virginia (USA, 1991) 1719-1723			bib

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