A logistic boosting approach to inducing multiclass alternating decision trees
Paper Title:
A logistic boosting approach to inducing multiclass alternating decision trees
Authored By:G. Holmes, B. Pfahringer, E.T. Frank, R.B. Kirkby and M.A. Hall
Number:01/02
Publisher:Department of Computer Science, The University of Waikato
Publication Date:2002
Pages:1-11
Abstract:The alternating decision tree (ADTree) is a successful classification technique that combine decision trees with the predictive accuracy of boosting into a ser to interpretable classification rules. The original formulation of the tree induction algorithm restricted attention to binary classification problems. This paper empirically evaluates several methods for extending the algorithm to the multiclass case by splitting the problem into several two-class LogitBoost procedure to induce alternating decision trees directly. Experimental results confirm that this procedure is comparable with methods that are based on the original ADTree formulation in accuracy, while inducing much smaller trees.
Bibtex Record:
@workingpaper{
Author = {Holmes, G. and Pfahringer, B. and Frank, E.T. and Kirkby, R.B. and Hall, M.A.},
Title = {A logistic boosting approach to inducing multiclass alternating decision trees},
Publisher = {Department of Computer Science, The University of Waikato},
Number = {01/02},
Pages = {1-11},
Abstract = {The alternating decision tree (ADTree) is a successful classification technique that combine decision trees with the predictive accuracy of boosting into a ser to interpretable classification rules. The original formulation of the tree induction algorithm restricted attention to binary classification problems. This paper empirically evaluates several methods for extending the algorithm to the multiclass case by splitting the problem into several two-class LogitBoost procedure to induce alternating decision trees directly. Experimental results confirm that this procedure is comparable with methods that are based on the original ADTree formulation in accuracy, while inducing much smaller trees.},
Year = {2002}
}
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