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A rough set approach for the discovery of classification rules in interval-valued information systems. (English) Zbl 1184.68409
Summary: A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects decision rules with a minimal set of features for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The minimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.
68T05Learning and adaptive systems
68T10Pattern recognition, speech recognition
68T37Reasoning under uncertainty
68U35Information systems (hypertext navigation, interfaces, decision support, etc.)
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