A dynamic discretization approach for constructing decision trees with a continuous label

IEEE Transactions on Knowledge and Data Engineering

Volumn 21, Issue 11, 2009, Pages 1505-1514

  Hu, Hsiao Wei ^a   Chen, Yen Liang ^a   Tang, Kwei ^b  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b PURDUE UNIVERSITY   (United States)

Author keywords

Classification.; Data mining; Decision trees

Indexed keywords

CLASS LABELS; CLASSIFICATION.; CONTINUOUS VARIABLES; DATA DISCRETIZATION; DECISION-TREE ALGORITHM; DISCRETIZATIONS; FINITE SET; PREPROCESSING APPROACHES; REGRESSION TREE ALGORITHMS; REGRESSION TREES; TREE ALGORITHMS; TREE INDUCTION;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DECISION TREES; DISCRETE EVENT SIMULATION; LABELS; SET THEORY;

DATA MINING;

ALGORITHMS; CLASSIFICATION; DATA BASES; DATA PROCESSING; DECISION MAKING; INFORMATION RETRIEVAL; REGRESSION ANALYSIS;

EID: 70350654056     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2009.24     Document Type: Article

References (33)

1. J.R. Cano, F. Herrera, and M. Lozano, "Evolutionary Stratified Training Set Selection for Extracting Classification Rules with Trade Off Precision-Interpretability," Data & Knowledge Eng., vol.60, no.1, pp. 90-108, 2007.
2. G. Jagannathan and R.N. Wright, "Privacy-Preserving Imputation of Missing Data," Data & Knowledge Eng., vol.65, no.1, pp. 40-56, 2008.
3. X.B. Li, J. Sweigart, J. Teng, J. Donohue, and L. Thombs, "A Dynamic Programming Based Pruning Method for Decision Trees," INFORMS J. Computing, vol.13, pp. 332-344, 2001.
4. S. Piramuthu, "Feature Selection for Financial Credit-Risk Evaluation Decisions," INFORMS J. Computing, vol.11, pp. 258-266, 1999.
5. F. Bonchi, F. Giannotti, G. Mainetto, and D. Pedreschi, "A Classification-Based Methodology for Planning Audit Strategies in Fraud Detection," Proc. Fifth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 175-184, 1999.
6. M.J.A. Berry and G.S. Linoff, Mastering Data Mining: The Art and Science of Customer Relationship Management. Wiley, 1999.
7. J.B. Ayers, Handbook of Supply Chain Management, second ed., pp. 426-427. Auerbach Publication, 2006.
8. M.R. Chmielewski and J.W. Grzymala-Busse, "Global Discretization of Continuous Attributes as Preprocessing for Machine Learning," Proc. Third Int'l Workshop Rough Sets and Soft Computing, pp. 294-301, 1994.
9. J. Cerquides and R.L. Mantaras, "Proposal and Empirical Comparison of a Parallelizable Distance-Based Discretization Method," Proc. Third Int'l Conf. Knowledge Discovery and Data Mining, pp. 139-142, 1997.
10. M.R. Chmielewski and J.W. Grzymala-Busse, "Global Discretization of Continuous Attributes as Preprocessing for Machine Learning," Int'l J. Approximate Reasoning, vol.15, pp. 319-331, 1996.
11. T. Van de Merckt, "Decision Tree in Numerical Attribute Space," Proc. 13th Int'l Joint Conf. Artificial Intelligence, pp. 1016-1021, 1993.
12. J. Dougherty, R. Kohavi, and M. Sahami, "Supervised and Unsupervised Discretization of Continuous Features," Proc. Int'l Conf. Machine Learning, pp. 194-202, 1995.
13. J. Bogaert, R. Ceulemans, and E.D. Salvador-Van, "Decision Tree Algorithm for Detection of Spatial Processes in Landscape Transformation," Environmental Management, vol.33, no.1, pp. 62-73, 2004.
14. L. Borzemski, "The Use of Data Mining to Predict Web Performance," Cybernetics and Systems, vol.37, no.6, pp. 587-608, 2006.
15. W. Desheng, "Detecting Information Technology Impact on Firm Performance Using DEA and Decision Tree," Int'l J. Information Technology and Management, vol.5, nos. 2/3, pp. 162-174, 2006.
16. S.A. Gaddam, V.V. Phoha, and K.S. Balagani, "K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods," IEEE Trans. Knowledge and Data Eng., vol.19, no.3, pp. 345-354, Mar. 2007.
17. R. Jin and G. Agrawal, "Efficient Decision Tree Construction on Streaming Data," Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 571-576, 2003.
18. M. Last, "Online Classification of Nonstationary Data Streams," Intelligent Data Analysis, vol.6, no.2, pp. 129-147, 2002.
19. M. Last, M. Friedman, and A. Kandel, "Using Data Mining for Automated Software Testing," Int'l J. Software Eng. and Knowledge Eng., vol.14, no.4, pp. 369-393, 2004.
20. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees. Chapman & Hall, 1993.
21. S. Kramer, "Structural Regression Trees," Proc. 13th Nat'l Conf. Artificial Intelligence, pp. 812-819, 1996.
22. J. Yang and J. Stenzel, "Short-Term Load Forecasting with Increment Regression Tree," Electric Power Systems Research, vol.76, nos. 9/10, pp. 880-888, June 2006.
23. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, A Practical Guide to Support Vector Classification, http://www.csie.ntu.edu.tw/∼cjlin/papers/ guide/guide.pdf, 2003.
24. S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, and K.R.K. Murthy, "Improvements to the SMO Algorithm for SVM Regression," IEEE Trans. Neural Networks, vol.11, no.5, pp. 1188-1193, Sept. 2000.
25. J.R. Quinlan, "Introduction of Decision Trees," Machine Learning, vol.1, pp. 81-106, 1986.
26. J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
27. P.M. Murphy and D.W. Aha, "UCI Repository of Machine Learning Database," http://archive.ics.uci.edu/ml/, 1994.
28. Y.L. Chen, C.L. Hsu, and S.C. Chou, "Constructing a Multi-Valued and Multi-Labeled Decision Tree," Expert Systems with Applications, vol.25, pp. 199-209, 2003.
29. J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001.
30. G.V. Kass, "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Applied Statistics, vol.29, pp. 119-127, 1980.
31. W.Y. Loh and Y.S. Shih, "Split Selection Methods for Classification Trees," Statistica Sinica, vol.7, pp. 815-840, 1997.
32. J.R. Quinlan, "Improved Use of Continuous Attributes in C4.5," Artificial Intelligence, vol.4, pp. 77-90, 1996.
33. J. Catlett, "Megainduction: Machine Learning on Very Large Databases," PhD thesis, Univ. of Sydney, 1991.