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- W2994820213 abstract "In this paper and in order to design a smart decision support system able to discriminate the community-acquired meningitis categories, we studied three different decision rule induction approaches. Two of these approaches are based on rough sets but differ in the number of stages in their processes and in the method used to generate rules. The first approach contains three stages namely continuous attributes' discretization, conditional features' reduction and decision rule induction and it applies the Indiscernibility Based Rule (IBR) algorithm. However, the second only includes two stages, which are discretization and decision rule generation, and utilizes the LEM2 (Learning from Examples Module, version 2) method. The third approach is similar to the second one except that it determines decision rules relied on AQ (Algorithm Quasi-optimal) or CN2 algorithm. The experiments were conducted on two multidimensional databases, one derived from the other, of clinical records concerning 310 cases of bacterial or viral meningitis. The obtained results showed that: combined with the Greedy Heuristic for Computing Decision and Approximate Decision Reducts (GHCADR) and the Equal Width Interval Discretization (EWID) methods, IBR algorithm offers the best performances in term of prediction accuracy, quality and compactness of generated decision rule sets." @default.
- W2994820213 created "2019-12-26" @default.
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- W2994820213 date "2019-10-02" @default.
- W2994820213 modified "2023-10-18" @default.
- W2994820213 title "Comparative study of decision rule induction approaches involving rough sets for meningitis categories' discrimination" @default.
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- W2994820213 doi "https://doi.org/10.1145/3368756.3369066" @default.
- W2994820213 hasPublicationYear "2019" @default.
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