Selecting Features by Vertical Compactness of Data
نویسندگان
چکیده
Feature selection is a data preprocessing step for classi cation and data mining tasks Tradition ally feature selection is done by selecting a mini mum number of features that determine the class label i e by the horizontal compactness of data In this paper we propose a new selection cri terion that aims at the vertical compactness of data In particular we select a subset of fea tures that yields the least number of projected instances while determining the class label A hybrid search that is partially DFS and partially BFS is proposed to exploit the pruning potential of the problem We compare the result induced by C before and after the feature selection
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