Averaging Over Decision Stumps

In this paper, we examine a minimum encoding approach to the inference of decision stumps. We then examine averaging over decision stumps as a method of generating probability estimates at the leaves of decision trees.

[1]  C. S. Wallace,et al.  An Information Measure for Classification , 1968, Comput. J..

[2]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[3]  J. Rissanen.  A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[4]  C. S. Wallace,et al.  A General Selection Criterion for Inductive Inference , 1984, ECAI.

[5]  Peter C. Cheeseman,et al.  In Defense of Probability , 1985, IJCAI.

[6]  C. S. Wallace,et al.  Estimation and Inference by Compact Coding , 1987 .

[7]  Chris Carter,et al.  Multiple decision trees , 2013, UAI.

[8]  Sholom M. Weiss,et al.  An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods , 1989, IJCAI.

[9]  Ronald L. Rivest,et al.  Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..

[10]  Lalit R. Bahl,et al.  A tree-based statistical language model for natural language speech recognition , 1989, IEEE Trans. Acoust. Speech Signal Process..

[11]  L. N. Kanal,et al.  Uncertainty in Artificial Intelligence 5 , 1990 .

[12]  T. Hastie,et al.  Shrinking Trees , 1990 .

[13]  Wray L. Buntine,et al.  A theory of learning classification rules , 1990 .

[14]  Trevor Hastie,et al.  Statistical Models in S , 1991 .

[15]  Leon Sterling,et al.  AI '92 : proceedings of the 5th Australian Joint Conference on Artificial Intelligence : Hobart, Tasmania, 16-18 November 1992 , 1992 .

[16]  Pat Langley,et al.  An Analysis of Bayesian Classifiers , 1992, AAAI.

[17]  Wray L. Buntine,et al.  Learning classification trees , 1992 .

[18]  Daryl Pregibon,et al.  Tree-based models , 1992 .

[19]  Pat Langley,et al.  Induction of One-Level Decision Trees , 1992, ML.