Feature Selection Based on Ant Colony Optimization for Cotton Foreign Fiber

Feature selection and feature extraction are the most important steps in classification systems. Feature selection is commonly used to reduce dimensionality of data sets. Selecting the suitable features to improve identifying speed and accuracy is a major problem of identifying foreign fiber in cotton. Therefore, feature selection is the most important step of identifying foreign fiber in cotton. To improve the performance of foreign fiber in cotton identifying, a novel feature selection algorithm based on ant colony optimization is presented. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. The algorithm is first to apply to features selection of foreign fibers in cotton. Proposed algorithm