Grey wolf optimization for one-against-one multi-class support vector machines

Grey Wolf Optimization (GWO) algorithm is a new meta-heuristic method, which is inspired by grey wolves, to mimic the hierarchy of leadership and grey wolves hunting mechanism in nature. This paper presents a hybrid model that employs grey wolf optimizer (GWO) along with support vector machines (SVMs) classification algorithm to improve the classification accuracy via selecting the optimal settings of SVMs parameters. The proposed approach consists of three phases; namely pre-processing, feature extraction, and GWO-SVMs classification phases. The proposed classification approach was implemented by applying resizing, remove background, and extracting color components for each image. Then, feature vector generation has been implemented via applying PCA feature extraction. Finally, GWO-SVMs model is developed for selecting the optimal SVMs parameters. The proposed approach has been implemented via applying One-againstOne multi-class SVMs system using 3-fold cross-validation. The datasets used for experiments were constructed based on real sample images of bell pepper at different stages, which were collected from farms in Minya city, Upper Egypt. Datasets of total 175 images were used for both training and testing datasets. Experimental results indicated that the proposed GWO-SVMs approach achieved better classification accuracy compared to the typical SVMs classification algorithm.