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Features play a very important role in the task of pattern classification. Consequently, the selection of suitable features is necessary as most of the raw data might be redundant or irrelevant to the recognition of patterns. In some cases, the classifier cannot perform well because of the large number of redundant features. This work presents a novel evolving feature selection algorithms taking the advantages of conditional dependency to improve the predictive accuracy. Bayes Conditionaldoi:10.21311/001.39.7.01 fatcat:wh5hvdjftzatxi56n7mryijzfe