A method for fuzzy rules extraction directly from numerical data and its application to pattern classification
Year: 1995
Type: article
Abstract: In this paper, we discuss a new method for extracting fuzzy rules directly from numerical input-output data for pattern classification. Fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region of data for a class and inhibition hyperboxes which inhibit the existence of data for that class. These rules are extracted from numerical data by recursively resolving overlaps between two classes. Then, optimal input variables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks using the Fisher iris data and a license plate recognition system for various examples.>
DOI: 10.1109/91.366565
Authors: S. Abe, Ming-Shong Lan
Institutions: Hitachi (Japan), Asahi Diamond Industrial Co., Ltd. (Japan)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2136035344