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A big challenge in the knowledge discovery process is to perform data pre-processing, specifically feature selection, on a large amount of data and high dimensional attribute set. A variety of techniques have been proposed in the literature to deal with this challenge with different degrees of success as most of these techniques need further information about the given input data for thresholding, need to specify noise levels or use some feature ranking procedures. To overcome thesedoi:10.1007/s10115-020-01467-y fatcat:4tsm4qlsdrddpnm3k5dzg2f7gi