Abstract
Feature selection as a common method of dimensionality reduction always is one of the hot topics in machine learning and data mining field. Classic algorithms don't consider features' global redundancies fully, which may cause classification accuracy on selected feature subset to be not high enough. For the weakness, we propose a feature selection method(FSCN) based on node importance estimation in complex networks and genetic algorithm, regarding each feature as a network node, creating edges according to mutual information, then the problem of feature selection is converted to estimate the node importance in complex networks, and choosing the best feature subset by genetic algorithm. As the experiment results show, our algorithm could find better feature selection subset which results in the lowdimensional data and the good classification accuracy.
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