Abstract
In this paper, a new feature selection method based on generalized data field (FR-GDF) is proposed. The goal of feature selection is selecting useful features and simultaneously excluding garbage features from a given feature set. It is Important to measure the “distance” between data points in existing feature selection approaches. To measure the “distance”, FR-GDF adopts potential value of data field. Information entropy of potential value is used to measure the inter-class distance and intra-class distance. This method eliminates unimportant or noise features of original feature sets and extracts the optional features. Experiments prove that FR-GDF algorithm performs well and is independent of the specific classification algorithm.
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Zhao, L., Wang, S., Lin, Y. (2014). A New Filter Approach Based on Generalized Data Field. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_25
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DOI: https://doi.org/10.1007/978-3-319-14717-8_25
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