Feature Selection Based on Ant Colony Optimization for Image Classification

Abstract:

In this paper, a feature selection algorithm based on ant colony optimization (ACO) is presented to construct classification rules for image classification. Most existing ACO-based algorithms use the graph with O(n2) edges. In contrast, the artificial ants in the proposed algorithm FSC-ACO traverse on a feature graph with only O(n) edges. During the process of feature selection, ants construct the classification rules for each class according to the improved pheromone and heuristic functions. FSC-ACO improves the qualities of rules depend on the classification accuracy and the length of rules. The experimental results on both standard and real image data sets show that the proposed algorithm can outperform the other related methods with fewer features in terms of speed, recall and classification accuracy.

• Periodical: Applied Mechanics and Materials (Volume 319)
• Pages: 337-342
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.319.337
• Online since: May 2013
• Authors: Li Tu, Li Zhi Yang
• Keywords: Ant Colony Optimization (ACO), Classification Rule, Feature Selection, Image Classification

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