Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
JADE-Based A-Team as a Tool for Implementing Population-Based Algorithms
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 03
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
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ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
A-Teams and Their Applications
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Distributed learning with data reduction
Transactions on computational collective intelligence IV
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The paper proposes an approach to data reduction. The data reduction procedures are of vital importance to machine learning and data mining. To solve the data reduction problems the agent-based population learning algorithm was used. The proposed approach has been used to reduce the original dataset in two dimensions including selection of reference instances and removal of irrelevant attributes. To validate the approach the computational experiment has been carried out. Presentation and discussion of experiment results conclude the paper.