A rough set approach for the discovery of classification rules in interval-valued information systems

Leung, Yee and Fischer, Manfred M. and Wu, Wei-Zhi and Mi, Ju-Sheng (2008): A rough set approach for the discovery of classification rules in interval-valued information systems. Published in: International Journal of Approximate Reasoning , Vol. 47, No. 2 (2008): pp. 233-246.

Abstract

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient 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 optimal 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Classification; Interval-valued information systems; Knowledge discovery; Knowledge reduction; Rough sets
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models

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