Classification trees with unbiased multiway splits

Journal of the American Statistical Association

Volumn 96, Issue 454, 2001, Pages 589-604

  Kim, Hyunjoong ^a   Loh, Wei Yin ^b  

^a Worcester Polytechnic Institute   (United States)

^b University of Wisconsin Madison   (United States)

Author keywords

Decision tree; Linear discriminant analysis; Missing value; Selection bias

Indexed keywords

EID: 1542573450     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1198/016214501753168271     Document Type: Article

References (37)

1. Breiman, L. (1998), “Arcing Classifiers” (with discussion), The Annals of Statistics, 26, 801–849.
2. Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984), classification and Regression Trees, New York: Chapman and Hall.
3. Brodley, C. E., and Utgoff, P. E. (1995), “Multivariate Decision Trees,” Machine Learning, 19, 45–77.
4. Buntine, W. (1992), “Learning classification Trees,” Statistics and Computing, 2, 63–73.
5. Cestnik, G., Konenenko, I., and Bratko, I. (1987), “Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users,” in Progress in Machine Learning, (eds). I. Bratko and N. Lavrac, Manhasset, NY: Sigma Press.
6. Clark, L. A., and Pregibon, D. (1993), “Tree-Based Models,” in Statistical Models in S, eds. J. M. Chambers and T. J. Hastie, New York: Chapman and Hall, pp. 377–419.
7. Diaconis, P., and Efron, B. (1983), “Computer-Intensive Methods in Statistics,” Scientific American, 248, 116–130.
8. Doyle, P. (1973), “The Use of Automatic Interaction Detector and Similar Search Procedures,” Operational Research Quarterly, 24, 465–467.
9. Fayyad, U. M. (1991), “On the Induction of Decision Trees for Multiple Concept Learning,” Ph.D. thesis, University of Michigan, Dept. of EECS.
10. Hastie, T., and Tibshirani, R. (1996), “Discriminant Analysis by Gaussian Mixtures,” Journal of the Royal Statistical Society, Ser. B, 58, 155–176.
11. Hastie, T., Buja, A., and Tibshirani, R. (1995), “Penalized Discriminant Analysis,” The Annals of Statistics, 23, 73–102.
12. Hastie, T., Tibshirani, R., and Buja, A. (1994), “Flexible Discriminant Analysis by Optimal Scoring,” Journal of the American Statistical Association, 89, 1255–1270.
13. Hawkins, D. M. (1997), “FIRM: Formal Inference-based Recursive Modeling, PC Version, Release 2.1,” Technical Report 546, University of Minnesota, School of Statistics.
14. Hochberg, Y., and Tamhane, A. C. (1987), Multiple Comparison Procedures, New York: Wiley.
15. Holte, R. C. (1993), “Very Simple classification Rules Perform Well on Most Commonly Used Datasets,” Machine Learning, 11, 63–90.
16. Kass, G. V. (1980), “An Exploratory Technique for Investigating Large Quantities of Categorical Data,” Applied Statistics, 29, 119–127.
17. Kohonen, T. (1995), Self-Organizing Maps, Heidelberg: Springer-Verlag.
18. Kooperberg, C., Bose, S., and Stone, C. J. (1997), “Polychotomous Regression,” Journal of the American Statistical Association, 92, 117–127.
19. Levene, H. (1960), “Robust Tests for Equality of Variances,” in Contributions to Probability and Statistics, eds. I. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, and H. B. Mann, Palo Alto, CA: Stanford University Press, pp. 278–292.
20. Lim, T.-S., Loh, W.-Y., and Shih, Y.-S. (2000), “A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New classification Algorithms,” Machine Learning, 40, 203–228.
21. Lock, R. H. (1993), “1993 New Car Data,” Journal of Statistics Education, 1(1).
22. Loh, W.-Y. (2001), “Regression Trees With Unbiased Variable Selection and Interaction Detection,” Statistica Sinica, to appear.
23. Loh, W.-Y., and Shih, Y.-S. (1997), “Split Selection Methods for classification Trees,” Statistica Sinica, 7, 815–840.
24. Loh, W.-Y., and Vanichsetakul, N. (1988), “Tree-Structured classification via Generalized Discriminant Analysis” (with comments), Journal of the American Statistical Association, 83, 715–728.
25. Merz, C. J., and Murphy, P. M. (1996), “UCI Repository of Machine Learning Databases,” University of California, Irvine, Dept. of Information and Computer Science, (http://www.ics.uci.edu/mlearn/MLRepository.html).
26. Müller, W., and Wysotzki, F. (1994), “Automatic Construction of Decision Trees for classification,” Annals of Operations Research, 52, 231–247.
27. Murthy, S. K., Kasif, S., and Salzberg, S. (1994), “A System for Induction of Oblique Decision Trees,” Journal of Artificial Intelligence Research, 2, 1–33.
28. Qu, P., and Loh, W.-Y. (1992), “Application of Box–Cox Transformations to Discrimination for the Two-Class Problem,” Communications in Statistics (Theory and Methods), 21, 2757–2774.
29. Quinlan, J. R. (1989), “Unknown Attribute Values in Induction,” in Proceedings of the Sixth International Machine Learning Workshop, pp. 164–168.
30. Quinlan, J. R. (1993), C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann.
31. Rouncefield, M. (1995), “The Statistics of Poverty and Inequality,” Journal of Statistics Education, 3(3).
32. Sarle, W. S. (1994), “Neural Networks and Statistical Models,” in Proceedings of the Nineteenth Annual SAS Users Groups International Conference, Cary, NC: SAS Institute, Inc., pp. 1538–1550.
33. Seber, G. A. F. (1984), Multivariate Observations, New York: Wiley.
34. Steinberg, D., and Colla, P. (1992), CART: A Supplementary Module for SYSTAT Evanston, IL: SYSTAT Inc.
35. Utgoff, P. E., Berkman, N. C., and Clouse, J. A. (1997), “Decision Tree Induction Based on Efficient Tree Restructuring,” Machine Learning, 29, 5–44.
36. White, A. P., and Liu, W. Z. (1994), “Bias in Information-Based Measures in Decision Tree Induction,” Machine Learning, 15, 321–329.
37. Zhang, H. (1998), Comment on “Bayesian CART model search,” Journal of the American Statistical Association, 93, 948–950.