Feature Selection in Hierarchical Feature Spaces

Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hierarchy, and further prunes the resulting feature set based on the features' relevance. We show that our method yields a good trade-off between feature space compression and classification accuracy, and outperforms both standard approaches as well as other approaches which also exploit hierarchies.