Simulated annealing: A pedestrian review of the theory and some applications
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Modern heuristic techniques for combinatorial problems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Algorithms for Feature Selection: An Evaluation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Selecting representative examples and attributes by a genetic algorithm
Intelligent Data Analysis
Adaptive Mechanisms for Classification Problems with Drifting Data
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Particle swarm optimization for prototype reduction
Neurocomputing
The COMPSET algorithm for subset selection
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Prototype selection algorithms for distributed learning
Pattern Recognition
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive integrated image segmentation and object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.