Comparing Tree-Simplification Procedures

Leonard A. Breslow, David W. Aha
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:67-74, 1997.

Abstract

Induced decision trees are frequently used by researchers in the machine learning and statistics communities to solve classification tasks (Breiman et al. 1984; Quinlan 1993). However, their practical utility is limited by difficulties users have in comprehending them due to their size and complexity. Many methods have been proposed to simplify decision trees, but their relative capabilities are largely unknown; their evaluation is usually limited to comparisons with "bench-mark" systems (e.g., C4.5, CART). This paper presents a categoriZation framework for tree-simplification methods and focuses on the empirical comparison of selected methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-breslow97a, title = {Comparing Tree-Simplification Procedures}, author = {Breslow, Leonard A. and Aha, David W.}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {67--74}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/breslow97a/breslow97a.pdf}, url = {https://proceedings.mlr.press/r1/breslow97a.html}, abstract = {Induced decision trees are frequently used by researchers in the machine learning and statistics communities to solve classification tasks (Breiman et al. 1984; Quinlan 1993). However, their practical utility is limited by difficulties users have in comprehending them due to their size and complexity. Many methods have been proposed to simplify decision trees, but their relative capabilities are largely unknown; their evaluation is usually limited to comparisons with "bench-mark" systems (e.g., C4.5, CART). This paper presents a categoriZation framework for tree-simplification methods and focuses on the empirical comparison of selected methods.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T Comparing Tree-Simplification Procedures %A Leonard A. Breslow %A David W. Aha %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-breslow97a %I PMLR %P 67--74 %U https://proceedings.mlr.press/r1/breslow97a.html %V R1 %X Induced decision trees are frequently used by researchers in the machine learning and statistics communities to solve classification tasks (Breiman et al. 1984; Quinlan 1993). However, their practical utility is limited by difficulties users have in comprehending them due to their size and complexity. Many methods have been proposed to simplify decision trees, but their relative capabilities are largely unknown; their evaluation is usually limited to comparisons with "bench-mark" systems (e.g., C4.5, CART). This paper presents a categoriZation framework for tree-simplification methods and focuses on the empirical comparison of selected methods. %Z Reissued by PMLR on 30 March 2021.
APA
Breslow, L.A. & Aha, D.W.. (1997). Comparing Tree-Simplification Procedures. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:67-74 Available from https://proceedings.mlr.press/r1/breslow97a.html. Reissued by PMLR on 30 March 2021.

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