Feature selection based on buzzard optimization algorithm for potato surface defects detection

Different methods of feature selection find the best subdivision from the candidate subset. In all methods, based on the application and the type of the definition, a subset is selected as the answer; which can optimize the value of an evaluation function. The large number of features, high spatial and temporal complexity, and even reduced accuracy are common problems in such systems. Therefore, research needs to be performed to optimize these systems. In this paper, for increasing the classification accuracy and reducing their complexity; feature selection techniques are used. In addition, a new feature selection method by using the buzzard optimization algorithm (BUOZA) is proposed. These features would be used in segmentation, feature extraction, and classification steps in related applications; to improve the system performance. The results of the performed experiment on the developed method have shown a high performance while optimizing the system’s working parameters.