Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Fadwa Alrowais1,*, Sunil Kumar3, Abdelhameed Ibrahim4, Abdelaziz A. Abdelhamid5,6
CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4027-4041, 2023, DOI:10.32604/cmc.2023.033039
Abstract In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead to instability. Because of… More >
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