Abstract
Feature selection is a well known and studied technique that aims to solve “the curse of dimensionality” and improve performance by removing irrelevant and redundant features. This paper highlights some well known approaches to filter feature selection, information theory and rough set theory, and compares a recent fitness function with some traditional methods. The contributions of this paper are two-fold. First, new results confirm previous research and show that the recent fitness function can also perform favorably when compared to rough set theory. Secondly, the measure of redundancy that is used in traditional information theory is shown to damage the performance when a similar approach is applied to the recent fitness function.
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Cervante, L., Gao, X. (2013). Information and Rough Set Theory Based Feature Selection Techniques. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds) Active Media Technology. AMT 2013. Lecture Notes in Computer Science, vol 8210. Springer, Cham. https://doi.org/10.1007/978-3-319-02750-0_17
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DOI: https://doi.org/10.1007/978-3-319-02750-0_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02749-4
Online ISBN: 978-3-319-02750-0
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