逐次ペア拡張による木構造データからの分類規則学習

概要

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A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from directed graph data by stepwise pair expansion (pairwise chunking). In this paper, we apply GBI to the classification problem by interpreting the root node as a class node and the links attached to it as the primary attributes. The node at the other end of each link is the value of the attribute, which has secondary attributes. Thus, each attribute can have its own attributes recursively, and the graph becomes a directed tree. In this case, the pairwise chunking must start at the root node and go backwards following the links. Though inducing classification rules by GBI has already been proposed, what is new in this paper is that we have changed the evaluation criterion in selecting the pair to be chunked and also added the pruning mechanism to GBI in order to avoid overfitting to the training data. The new classification version of GBI, called CL-GBI, is applied to two kinds of problems. One is the command prediction problem where CL-GBI must predict what the user wants to do next, and the other is the WWW browsing problem where CL-GBI must extract chracteristic routes from the access log, i. e. the browsing history of many users. The results indicates the effectiveness of our approach.
社団法人人工知能学会の論文
2000-05-01