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- W3028704087 abstract "Although huge number of features and instances give more information about the data yet it presents irrelevant and superfluous (noisy) features and instances. Therefore, there is a need to remove such type of features and instances for efficient classification. The more appropriate term used for simultaneous reduction of features and instances, called dual selection, leads to faster classification without sacrificing much for the classifier’s performance. In the current paper, we suggest a parallel genetic algorithm (PGA) for dual selection that allows an efficient selection of features as well as instances. The paper encapsulates experimental analysis on ten medium- and large-sized datasets. The results confirm the superiority of proposed PGA in terms of higher accuracy along with considerable reduction rates." @default.
- W3028704087 created "2020-06-05" @default.
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- W3028704087 date "2020-01-01" @default.
- W3028704087 modified "2023-09-26" @default.
- W3028704087 title "A Proposal for Dual Data Selection Using Parallel Genetic Algorithm" @default.
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- W3028704087 doi "https://doi.org/10.1007/978-981-15-3643-4_16" @default.
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