Abstract
Population-based (or evolutionary) algorithms have been attracting an increased attention due to their powerful search capabilities. For the particular problem of feature selection, population-based methods aim to produce better ”or fitter” future generations that contain more informative subsets of features. It is well-known that feature subset selection is a very challenging optimization problem, especially when dealing with datasets that contain large number of features. Most of the commonly used population-based feature selection methods use operators that do not take into account relationships between features to generate future subsets, which can have an impact on their capabilities to properly explore the search space. We present here a new population-based feature selection method that utilize dependency between features to guide the search. In addition, a novel method for estimating dependency between feature pairs is proposed based on the concept of fuzzy entropy. Results obtained from datasets with various sizes indicate the superiority of the proposed method in comparison to some of the well-known methods in the literature.
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Al-Ani, A., Khushaba, R.N. (2012). A Population Based Feature Subset Selection Algorithm Guided by Fuzzy Feature Dependency. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_43
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DOI: https://doi.org/10.1007/978-3-642-35326-0_43
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