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• W2030137593 abstract "A relabeling algorithm for retrieval of noisy instances with binary outcomes is presented. The relabeling algorithm iteratively retrieves, selects, and re-labels data instances (i.e., transforms a decision space) to improve prediction quality. It emphasizes knowledge generalization and confidence rather than classification accuracy. A confidence index incorporating classification accuracy, prediction error, impurities in the relabeled dataset, and cluster purities was designed. The proposed approach is illustrated with a binary outcome dataset and was successfully tested on the standard benchmark four UCI repository dataset as well as bladder cancer immunotherapy data. A subset of the most stable instances (i.e., 7% to 51% of the sample) with high confidence (i.e., between 64%–99.44%) was identified for each application along with most noisy instances. The domain experts and the extracted knowledge validated the relabeled instances and corresponding confidence indexes. The relabeling algorithm with some modifications can be applied to other medical, industrial, and service domains." @default.
• W2030137593 created "2016-06-24" @default.
• W2030137593 creator A5033746770 @default.
• W2030137593 creator A5040161515 @default.
• W2030137593 date "2010-03-01" @default.
• W2030137593 modified "2023-10-16" @default.
• W2030137593 title "Relabeling algorithm for retrieval of noisy instances and improving prediction quality" @default.
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• W2030137593 doi "https://doi.org/10.1016/j.compbiomed.2009.12.005" @default.
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