Abstract
Feature selection is a universal combinatorial optimization problem that is used to enhance the characteristics of high-dimensional datasets by eliminating redundant data and selecting prominent features to generate acceptable classification performance. In optimization problems, the objective function can have several local optima, but the ultimate aim is to identify global optima or values close to global optimum. The Crow Search Algorithm (CSA) is a recently suggested metaheuristic algorithm, implemented to feature selection issues systematically. It is witnessed that the CSA solution search equation is suitable in exploration but poor in exploitation. In this paper, a global best-guided solution to CSA (G-CSA) is proposed and applied to pick the optimum feature subset in a wrapper mode to boost exploitation for classification purposes. The efficiency of the proposed methodology is examined on twelve standard UCI datasets. When comparing experimental results, it is clear that the proposed algorithm is superior to its challengers.
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Agarwal, U., Sahu, T.P. (2022). Global Best Guided Binary Crow Search Algorithm for Feature Selection. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_45
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DOI: https://doi.org/10.1007/978-981-19-2281-7_45
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