Low-level segmentation of aerial images with fuzzy clustering
IEEE Transactions on Systems, Man and Cybernetics
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
A new mutual information based measure for feature selection
Intelligent Data Analysis
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Unsupervised gene selection and clustering using simulated annealing
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Efficient Feature Selection Using Ant Colony Optimization Algorithm
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
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Feature selection is an indispensable pre-processing step when mining huge datasets that can significantly improve the overall system performance. This paper presents a novel feature selection method that utilizes both the Ant Colony Optimization (ACO) and fuzzy memberships. The algorithm estimates the local importance of subsets of features, i.e., their pheromone intensities by utilizing fuzzy c-means (FCM) clustering technique. In order to prove the effectiveness of the proposed method, a comparison with another powerful ACO based feature selection algorithm that utilizes the Mutual Information (MI) concept is presented. The method is tested on two biosignals driven applications: Brain Computer Interface (BCI), and prosthetic devices control with myoelectric signals (MES). A linear discriminant analysis (LDA) classifier is used to measure the performance of the selected subsets in both applications. Practical experiments prove that the new algorithm can be as accurate as the original method with MI, but with a significant reduction in computational cost, especially when dealing with huge datasets.