Supervised Fuzzy Clustering for the Identification of Fuzzy Classifiers
The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper a new fuzzy model structure is proposed where each rule can represent more than one classes with different probabilities. The obtained classifier can be considered as an extension of the quadratic Bayes classifier that utilizes mixture of models for estimating the class conditional densities. A supervised clustering algorithm has been worked out for the identification of this fuzzy model. The relevant input variables of the fuzzy classifier have been selected based on the analysis of the clusters by Fisher's interclass separability criteria. This new approach is applied to the well-known wine and Wisconsin Breast Cancer classification problems.
It is also desribed in:
J. Abonyi, F. Szeifert, Supervised fuzzy clustering for the identification of fuzzy classifiers, Pattern Recognition Letters, 24(14) 2195-2207, October 2003
For more MATLAB tools please visit:
http://www.abonyilab.com/software-and-data
인용 양식
Janos Abonyi (2024). Supervised Fuzzy Clustering for the Identification of Fuzzy Classifiers (https://www.mathworks.com/matlabcentral/fileexchange/47203-supervised-fuzzy-clustering-for-the-identification-of-fuzzy-classifiers), MATLAB Central File Exchange. 검색됨 .
MATLAB 릴리스 호환 정보
플랫폼 호환성
Windows macOS Linux카테고리
- Control Systems > Fuzzy Logic Toolbox >
- Industries > Biotech and Pharmaceutical > Genomics and Next Generation Sequencing >
태그
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!버전 | 게시됨 | 릴리스 정보 | |
---|---|---|---|
1.0.0.0 |