Learning Decision Lists over Tree Patterns and Its Application

Abstract

This paper introduces a new concept , a decision tree (or list) over tree patterns, which is a natural extension of a decision tree (or decision list) , for dealing with tree structured objects. The learnability of this class is studied within the framework of the Probably Approximately Correct learning model and the Identification in the Limit model. It is found that the class k-node-DLRTP, a subclass of decision lists over regular tree patterns, is not polynomial time PAC learnable if NP:fRP , but that the class knode-DLRTP(m), a subclass ofk-node-DLRTP where the number of variables in each tree pattern is bounded by m, is polynomial time PAC learnable. We also show that the class of decision lists over regular tree patterns is polynomial time inferable in the limit. Then we propose a more practical learning algorithm which employs heuristics based on an information theoretical evaluation function. This algorithm is applied to learning control strategies of rules in various domains , and its effectiveness is shown.