Learning classification trees (1992)
| Venue: | Statistics and Computing |
| Citations: | 112 - 8 self |
BibTeX
@ARTICLE{Buntine92learningclassification,
author = {Wray Buntine and Wray Buntine},
title = {Learning classification trees},
journal = {Statistics and Computing},
year = {1992},
volume = {2},
pages = {63--73}
}
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Abstract
Algorithms for learning cIassification trees have had successes in ar-tificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statis-tics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to QuinIan’s information gain, while smoothing and averaging replace pruning. Comparative ex-periments with reimplementations of a minimum encoding approach, Quinlan’s C4 (1987) and Breiman et aL’s CART (1984) show the full Bayesian algorithm produces more accurate predictions than versions
