A Probabilistic Substructure-Based Approach for Graph Classification

The classification of graph based objects is an important challenge from a knowledge discovery standpoint and has attracted considerable attention recently. In this paper, we present a probabilistic substructure-based approach for classifying a graphbased dataset. More specifically, we use a frequent subgraph mining algorithm to construct substructure based descriptors and apply the maximum entropy principle to convert the local patterns into a global classification model for graph data. Empirical studies conducted on real world data sets showed that the maximum entropy substructure-based approach often outperforms existing feature vector methods using AdaBoost and Support Vector Machine.

• Type: Conference
• Year: 2007
• Where: ICTAI
• Authors: H. D. K. Moonesinghe, Hamed Valizadegan, Samah Jamal Fodeh, Pang-Ning Tan
• Keywords: Artificial Intelligence, ICTAI 2007, Maximum Entropy, Maximum Entropy Principle, Substructure-based Approach