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• W3164271353 abstract "In this paper, the application of the swarm intelligence algorithm for feature selection challenges in the field of machine learning is presented. Feature selection, which is part of the dimension reduction process, helps in selecting those features from the data set that have the most significant impact on the performance and accuracy of the machine learning model. Since the feature selection searches for an optimal (sub-optimal) set of features in a large search area and bearing in mind that swarm intelligence algorithms have proven to be good optimizers for tackling these kinds of problems, they can be efficiently employed as a wrapper method for feature selection. This manuscript proposes the improved implementation of a widely used firefly algorithm, adapted for tackling this important and current problem. Observed drawbacks of the original firefly algorithm are overcome by introducing a quasi-reflection-based learning procedure in the initialization phase. The proposed method was validated against 21 datasets, while the k-nearest neighbors were used as a classification model. The enhanced approach is compared with other state-of-the-art algorithms validated on the same data sets and it has been shown that the proposed implementation overcomes others in terms of features’ number, as well as in the terms of classification accuracy." @default.
• W3164271353 created "2021-06-07" @default.
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• W3164271353 date "2021-05-26" @default.
• W3164271353 modified "2023-10-16" @default.
• W3164271353 title "Feature Selection by Firefly Algorithm with Improved Initialization Strategy" @default.
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• W3164271353 doi "https://doi.org/10.1145/3459960.3459974" @default.
• W3164271353 hasPublicationYear "2021" @default.
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