PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayes optimal classification for decision trees
Siegfried Nijssen
In: ICML 2008, 5-9 Jun 2008, Helsinki, Finland.

Abstract

We present an algorithm for exact Bayes optimal classification from a hypothesis space of decision trees satisfying leaf constraints. Our contribution is that we reduce this classification problem to the problem of finding a rule-based classifier with appropriate weights. We show that these rules and weights can be computed in linear time from the output of a modified frequent itemset mining algorithm, which means that we can compute the classifier in practice, despite the exponential worst-case complexity. In experiments we compare the Bayes optimal predictions with those of the maximum a posteriori hypothesis.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5321
Deposited By:Siegfried Nijssen
Deposited On:24 March 2009