Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction
Chunkingless Graph-Based Induction (Cl-GBI) is a machine learning technique proposed for the purpose of extracting typical patterns from graph-structured data. This method is regarded as an improved version of Graph-Based Induction (GBI) which employs stepwise pair expansion (pairwise chunking) to extract typical patterns from graph-structured data, and can find overlapping patterns that cannot not be found by GBI. In this paper, we propose an algorithm for constructing decision trees for graph-structured data using Cl-GBI. This decision tree construction algorithm, called Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI), can construct decision trees from graph-structured datasets while simultaneously constructing attributes useful for classification using Cl-GBI internally. Since patterns extracted by Cl-GBI are considered as attributes of a graph, and their existence/non-existence are used as attribute values, DT-ClGBI can be conceived as a tree generator equipped with feature construction capability. Experiments were conducted on synthetic and real-world graph-structured datasets showing the effectiveness of the algorithm.
KeywordsDecision Tree Root Node Predictive Accuracy Test Pattern Input Graph
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