International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Applied Intelligence
Evolving Fuzzy Rule Based Classifiers with GA-P: A Grammatical Approach
Proceedings of the Second European Workshop on Genetic Programming
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
A fuzzy classifier with ellipsoidal regions for diagnosis problems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy classifier with ellipsoidal regions
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Fast and efficient colour inspection using sets of ellipsoidal regions
Machine Graphics & Vision International Journal
| Hi-index | 0.10 |
This paper presents an adaptive classification method that utilizes ellipsoidal regions for multidimensional pattern classification problems with continuous input variables. The classification method fits a finite number of the ellipsoidal regions to data pattern by using adaptive operations iteratively. The method adaptively expands, rotates, shrinks, and/or moves the ellipsoidal regions while each ellipsoidal region is separately handled with a fitness value assigned. The adaptation procedure is combined with a variable selection process in the outer loop, where significant input variables for the ellipsoids are determined by using a stepwise selection method. The performance of the method is evaluated on well-known classification problems from the UCI machine learning repository. The evaluation result shows that the proposed method can exert equivalent or superior performance, with smaller number of rules, to other classification methods such as fuzzy rules, decision trees, or neural networks.