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- W3021752156 abstract "We have already seen applications of nature-inspired algorithms to a wide variety of optimization problems. This chapter discusses another field of application which has not quite been explored to great depths yet. The problem of feature selection is approached by employing the bat algorithm on a medical dataset of white blood cells (WBCs). Feature selection, from a bird's eye view, is choosing an optimal subset of features from all features such that it does not hinder the classification accuracy. Since the differential count of WBCs is a key aspect for medical diagnostics and analysis, feature selection gives the advantage of faster processing and computing. The proposed methodology selects 11 features out of 35 features and has an accuracy of 97.69%. The chapter concludes with a detailed comparison with other recent nature-inspired algorithms, including the optimized crow search algorithm and the optimized cuttlefish algorithm." @default.
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- W3021752156 date "2020-01-01" @default.
- W3021752156 modified "2023-09-27" @default.
- W3021752156 title "Bat-inspired algorithm for feature selection and white blood cell classification" @default.
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- W3021752156 doi "https://doi.org/10.1016/b978-0-12-819714-1.00022-1" @default.
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