Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Consistency-based search in feature selection
Artificial Intelligence
Ant Colony Optimization
A model based on ant colony system and rough set theory to feature selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
Two step ant colony system to solve the feature selection problem
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Finding minimal rough set reducts with particle swarm optimization
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Rough sets in the Soft Computing environment
Information Sciences: an International Journal
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This paper introduces the Dynamic Mesh Optimization meta-heuristic, which falls under the evolutionary computation techniques. Moreover, we outline its application to the feature selection problem. A set of nodes representing subsets of features makes up a mesh which dynamically grows and moves across the search space. The novel methodology is compared with other existing meta-heuristic approaches, thus leading to encouraging empirical results.