Abstract — This paper proposed an application of Binary Particle Swarm Optimization in automatic classification of wood species. The images of wood species are taken from Universiti Teknologi Malaysia’s CAIRO Wood Database which consists of 25 species. The features of the images are extracted using Gray Level Co-Occurrence Matrix. Then, Binary Particle Swarm Optimization is use to optimize feature selection and parameters related to it. The result indicates that the proposed approach obtained a better result compared to previous literatures with fewer features used as input for the classifier. Index Terms — binary particle swarm optimization; computational intelligence; gray level co-occurrence matrix; k-nereast neighbour; optimization; pattern recognition; wood recogniiton 1
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