A finite-sample simulation study of cross validation in tree-based models

Information Technology and Management

Volumn 10, Issue 4, 2009, Pages 223-233

  Kim, Seoung Bum ^a   Huo, Xiaoming ^b   Tsui, Kwok Leung ^b  

^a Korea University   (South Korea)

^b Georgia Institute of Technology   (United States)

Author keywords

Bayes classifier; Cross validation; Trees based models

Indexed keywords

EID: 74149086886     PISSN: 1385951X     EISSN: 15737667     Source Type: Journal    
DOI: 10.1007/s10799-009-0052-7     Document Type: Conference Paper

References (19)

1. M. Anthony, S.B. Holden, Cross-validation for binary classification by real-valued functions: theoretical analysis, in Proceedings of the 11th Annual Conference on Computational Learning Theory, (1998) pp. 218-229
2. L Breiman P Spector 1992 Submodel selection and evaluation in regression: the X-random case Int. Stat. Rev. 60 3 291 319 10.2307/1403680
3. L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and Regression Trees (Wadsworth International Group, Belmont, 1984)
4. LP Devroye TJ Wagner 1979 Distribution-free inqualities for the deleted and holdout error estimates IEEE Trans. Inf. Theory 25 2 202 207 10.1109/TIT.1979.1056032
5. LP Devroye TJ Wagner 1979 Distribution-free performance bounds for potential function rules IEEE Trans. Inf. Theory 25 5 601 604 10.1109/TIT.1979.1056087
6. B Efron 1983 Estimating the error rate of a prediction rule: improvement of cross-validation J. Am. Stat. Assoc. 78 382 316 331 10.2307/2288636
7. B Efron 1986 How biased is the apparent error rate of a prediction rule? J. Am. Stat. Assoc. 81 394 461 470 10.2307/2289236
8. T. Hastie, R. Tibshirani, J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, New York, 2001)
9. X Huo SB Kim K-L Tsui S Wang 2006 FBP: a frontier-based tree-pruning algorithm INFORMS J. Comput. 18 4 494 505 10.1287/ijoc.1050.0133 Fall
10. M Kearns D Ron 1999 Algorithmic stability and sanity check bounds for leave-one-out cross validation bounds Nueral Comput. 11 6 1427 1453 10.1162/089976699300016304
11. R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection, in International Joint Conference of Artificial Intelligence (IJCAI) (1995), pp. 1137-1145.
12. WH Rogers TJ Wagner 1978 A finite sample distribution-free performance bound for local discimination rule Ann. Stat. 6 506 514 10.1214/aos/1176344196
13. J Shao 1993 Linear-model selection by cross-validation J. Am. Stat. Assoc. 88 422 486 494 10.2307/2290328
14. J Shao 1996 Bootstrap model selection J. Am. Stat. Assoc. 91 434 655 665 10.2307/2291661
15. J Shao 1998 Convergence rates of the generalization information criterion J. Nonparametr. Stat. 9 3 217 225 10.1080/10485259808832743
16. M. Stone, Cross-validatory choice and assessment of statistical prediction. J. Roy. Stat. Soc. B 36(2), 111-133 (1974)
17. P Zhang 1992 On the distributional properties of model selection criteria J. Am. Stati. Assoc. 87 419 732 737 10.2307/2290211
18. P Zhang 1993 Model selection via multifold cross validation Ann. Stat. 21 1 299 313 10.1214/aos/1176349027
19. P Zhang 1993 On the convergence rate of model selection criteria Commun. Stat. Theory Methods 22 10 2765 2775 10.1080/03610929308831184