Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Machine Learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Approximative fuzzy rules approaches for classification with hybrid-GA techniques
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Journal of Global Optimization
Classification by Voting Feature Intervals
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Knowledge in Context: A Strategy for Expert System Maintenance
AI '88 Proceedings of the 2nd Australian Joint Artificial Intelligence Conference
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Differential Evolution as a viable tool for satellite image registration
Applied Soft Computing
Influence of crossover on the behavior of Differential Evolution Algorithms
Applied Soft Computing
Differential Evolution for automatic rule extraction from medical databases
Applied Soft Computing
Expert Systems with Applications: An International Journal
International Journal of Data Mining and Bioinformatics
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Differential Evolution, a version of an Evolutionary Algorithm, is used to perform automatic classification of handsegmented image parts collected in a seven–class database. Our idea is to exploit it to find the positions of the class centroids in the search space such that for any class the average distance of instances belonging to that class from the relative class centroid is minimized. The performance of the resulting best individual is computed in terms of error rate on the testing set. Then, such a performance is compared against those of other ten classification techniques well known in literature. Results show the effectiveness of the approach in solving the classification task.