A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems
Author | : Yee Leung |
Publisher | : |
Total Pages | : 32 |
Release | : 2017 |
ISBN-10 | : OCLC:1305400628 |
ISBN-13 | : |
Rating | : 4/5 (28 Downloads) |
Download or read book A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems written by Yee Leung and published by . This book was released on 2017 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.