Adaptive fuzzy rule-based classification systems

This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure, and an additional learning procedure. The error correc...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on fuzzy systems 1996-08, Vol.4 (3), p.238-250
Main Authors: Nozaki, K., Ishibuchi, H., Tanaka, H.
Format: Article
Language:English
Subjects:
Adaptative systems
Applied sciences
Automatic control
Computer science
control theory
systems
Control systems
Control theory. Systems
Error correction
Exact sciences and technology
Fuzzy control
Fuzzy systems
Humans
Iris
Pattern classification
Pattern matching
Testing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure, and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classification performance. That is, when a pattern is misclassified by a particular fuzzy rule, the grade of certainty of that rule is decreased. On the contrary, when a pattern is correctly classified, the grade of certainty is increased. Because the error correction-based learning procedure is not meaningful after all the given patterns are correctly classified, we cannot adjust a classification boundary in such a case. To acquire a more intuitively acceptable boundary, we propose an additional learning procedure. We also propose a method for selecting significant fuzzy rules by pruning unnecessary fuzzy rules, which consists of the error correction-based learning procedure and the concept of forgetting. We can construct a compact fuzzy rule-based classification system with high performance.
ISSN:1063-6706
1941-0034
DOI:10.1109/91.531768