Multi-label Feature Selection for Graph Classification

Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug activity predictions and kinase inhibitor discovery. Current research on graph classification focuses on singlelabel settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously. Extracting good features using multiple labels of the graphs becomes an important step before graph classification. In this paper, we study the problem of multi-label feature selection for graph classification and propose a novel solution, called gMLC, to efficiently search for optimal subgraph features for graph objects with multiple labels. Different from existing feature selection methods in vector spaces which assume the feature set is given, we perform multi-label feature selection for graph data in a progressive way together with the subgraph feature mining process. We derive...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Xiangnan Kong, Philip S. Yu
• Keywords: Data Mining, Feature Selection, Graph Classification, ICDM 2010, Multiple Labels