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[2D1-GS-2-02] Expansion of GNMiner: Discovery of Rules Considering Individuality Based on Continuous Attribute Values
Keywords:Evolutionary Computation, Knowledge Discovery, Knowledge Acquisition, Individuality
This study enhances the data mining tool “GNMiner” through Genetic Network Programming (GNP) for rule discovery, focusing on individuality with flexible discretization of continuous attributes.
Unlike traditional models with predefined thresholds for the whole dataset, leading to rigidity and inefficiency, this approach customizes discretization for individual cases before classification and prediction.
This method fosters efficient, swift rule discovery in datasets with continuous attributes, ensuring maximum consideration of individuality without the process of matching.
Unlike traditional models with predefined thresholds for the whole dataset, leading to rigidity and inefficiency, this approach customizes discretization for individual cases before classification and prediction.
This method fosters efficient, swift rule discovery in datasets with continuous attributes, ensuring maximum consideration of individuality without the process of matching.
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