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- W2021461290 abstract "The major weakness of associative classification is examined. A novel approach for classifying imbalanced dataset is proposed. It is an associative classification. Rules which are un-frequent are used to build the classifier rule set. Besides the confidence of pattern “X→Y”, the confidence of pattern “Y→X” is used in the approach. Further more, only features of rare classes are preserved while training. The good performance of the approach is shown by the experiments." @default.
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- W2021461290 date "2009-01-01" @default.
- W2021461290 modified "2023-09-26" @default.
- W2021461290 title "A Novel Approach to Classify Imbalanced Dataset Based on Rare Attributes and Double Confidences" @default.
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- W2021461290 doi "https://doi.org/10.1109/wkdd.2009.20" @default.
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