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- W200855749 abstract "In this paper we evaluate the performance of Weighted Associative Classifier and propose the theoretical concept of Fuzzy Weighted Associative Classifier (FWAC). Associative classifiers, the new classification approach is especially fit to applications where the model assists the domain (Medical diagnosis) experts in their decisions making. Weighted Associative Classifiers that takes advantage of weighted association rule mining is already being proposed [1]. The experiments on bench mark dataset reveals that WAC is a promising alternative in medical prediction with improved accuracy over the other associative classifiers and certainly deserves further attention. Further there is so-called a sharp boundary problem in association rules mining with quantitative attribute domains. To solve this problem we use fuzzy logic and partition the domains. The concept of Fuzzy Weighted Support and Fuzzy Weighted Confidence will be used to generate fuzzy weighted Classification Association Rules(CAR), which will ultimately used for prediction." @default.
- W200855749 created "2016-06-24" @default.
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- W200855749 date "2011-01-01" @default.
- W200855749 modified "2023-10-02" @default.
- W200855749 title "Performance Evaluation of Weighted Associative Classifier in Health Care Data Mining and Building Fuzzy Weighted Associative Classifier" @default.
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- W200855749 doi "https://doi.org/10.1007/978-3-642-24037-9_22" @default.
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