Structure feature selection for graph classification

With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining and machine learning community. Towards building highly accurate classification models for graph data, here we present an efficient graph feature selection method. In our method, we use frequent subgraphs as features for graph classification. Different from existing methods, we consider the spatial distribution of the subgraph features in the graph data and select those ones that have consistent spatial location. We have applied our feature selection methods to several cheminformatics benchmarks. Our method demonstrates a significant improvement of prediction as compared to the state-of-the-art methods. Categories and Subject Descriptors H.2.8 [Database Management]: Database ApplicationsData Mining General Terms Algorithms, Experimentation Keywords Data Mining, Classification, Feature Selection

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Hongliang Fei, Jun Huan
• Keywords: CIKM 2008, Efficient Graph, Feature Selection Methods, Graph Data, Information Management