Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A Recursive Partitioning Rule for Binary Decision Trees

  • Published : 2003.08.01
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Abstract

In this paper, we reconsider the Kolmogorov-Smirnoff distance as a split criterion for binary decision trees and suggest an algorithm to obtain the Kolmogorov-Smirnoff distance more efficiently when the input variable have more than three categories. The Kolmogorov-Smirnoff distance is shown to have the property of exclusive preference. Empirical results, comparing the Kolmogorov-Smirnoff distance to the Gini index, show that the Kolmogorov-Smirnoff distance grows more accurate trees in terms of misclassification rate.

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References

  1. IEEE Transactions on Pattern Analysis and Machine Intelligence v.19 A new criterion in selection and discretization of attributes for generation of decision trees 전병환;김창수;송홍엽;김재희 https://doi.org/10.1109/34.643896
  2. Classification and Regression Trees Breiman,L.;Friedman,J.H.;Olshen,R.A.;Stone,C.J.
  3. Statistical Models in S Tree-based models Clark,L.A.;Pregibon,D.;J.M.Chambers(ed.);T.J.Hastie(ed.)
  4. IEEE Transactions of Computers v.C-26 A recursive partitioning decision rule for nonparametric classification Friedman,J.H. https://doi.org/10.1109/TC.1977.1674849
  5. Journal of the American Statistical Association v.49 Measures of association for cross-classifications Goodman,L.A.;Kruskal,W.H. https://doi.org/10.2307/2281536
  6. Annals of Statistics v.6 Asymptotically efficient, computationally feasible solutions to the classification problem Gordon,L.;Olshen,R.A. https://doi.org/10.1214/aos/1176344197
  7. Applied Statistics v.29 An exploratory technique for investigation large quantities of categorical data Kass,G.V. https://doi.org/10.2307/2986296
  8. UCI Repository of Machine Learning Databases Merz,C.J.;Murphy,P.M.
  9. Statistics and Computing v.7 A fast splitting procedure for classification trees Mola,F.;Siciliano,R. https://doi.org/10.1023/A:1018590219790
  10. C4.5: Programs for Machine Learning Quinlan,J.R.
  11. Pattern Recognition v.12 A combined nonparametric approach to feature selection and binary decision tree design Rounds,E.M. https://doi.org/10.1016/0031-3203(80)90029-1
  12. Statistics & Probability Letters v.54 Selecting the best splits for classification trees with categorical variables Shih,Y.S. https://doi.org/10.1016/S0167-7152(00)00188-7
  13. Statistics and Computing v.3 Block diagrams and splitting criteria for classification trees Taylor,P.C.;Silverman,B.W. https://doi.org/10.1007/BF00141771