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• W2206296687 abstract "Uncertainty measure mining and applications are fundamental, and it is possible for double-quantitative fusion to acquire benign measures via heterogeneity and complementarity. This paper investigates the double-quantitative fusion of relative accuracy and absolute importance to provide systematic measure mining, benign integration construction, and hierarchical attribute reduction. (1) First, three-way probabilities and measures are analyzed. Thus, the accuracy and importance are systematically extracted, and both are further fused into importance-accuracy (IP-Accuracy), a synthetic causality measure. (2) By sum integration, IP-Accuracy gains a bottom-top granulation construction and granular hierarchical structure. IP-Accuracy holds benign granulation monotonicity at both the knowledge concept and classification levels. (3) IP-Accuracy attribute reduction is explored based on decision tables. A hierarchical reduct system is thereby established, including qualitative/quantitative reducts, tolerant/approximate reducts, reduct hierarchies, and heuristic algorithms. Herein, the innovative tolerant and approximate reducts quantitatively approach/expand/weaken the ideal qualitative reduct. (4) Finally, a decision table example is provided for illustration. This paper performs double-quantitative fusion of causality measures to systematically mine IP-Accuracy, and this measure benignly constructs a granular computing platform and hierarchical reduct system. By resorting to a monotonous uncertainty measure, this study provides an integration-evolution strategy of granular construction for attribute reduction." @default.
• W2206296687 created "2016-06-24" @default.
• W2206296687 creator A5024791074 @default.
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• W2206296687 date "2016-01-01" @default.
• W2206296687 modified "2023-10-04" @default.
• W2206296687 title "Double-quantitative fusion of accuracy and importance: Systematic measure mining, benign integration construction, hierarchical attribute reduction" @default.
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• W2206296687 doi "https://doi.org/10.1016/j.knosys.2015.09.001" @default.
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