Competitive learning algorithms for vector quantization
Neural Networks
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Self-Organizing Maps
Image classification: an evolutionary approach
Pattern Recognition Letters
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The science of breeding and its application to the breeder genetic algorithm (bga)
Evolutionary Computation
Search-intensive concept induction
Evolutionary Computation
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
A genetic algorithm-based approach to cost-sensitive bankruptcy prediction
Expert Systems with Applications: An International Journal
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An evolutionary computation based algorithm for data classification is presented. The proposed algorithm refers to the learning vector quantization paradigm and is able to evolve sets of points in the feature space in order to find the class prototypes. The more remarkable feature of the devised approach is its ability to discover the right number of prototypes needed to perform the classification task without requiring any a priori knowledge on the properties of the data analyzed. The effectiveness of the approach has been tested on satellite images and the obtained results have been compared with those obtained by using other classifiers.