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

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Abstract

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.

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  1. Binary first mode

  2. Binary second mode

  3. Binary third mode

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Authors and Affiliations

  1. Department of Electrical Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran

    Ali Arshaghi & Leila Ghabeli

  2. Department of Electrical Engineering, Islamic Azad University, Majlesi Branch, Isfahan, Iran

    Mohsen Ashourian

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  1. Ali Arshaghi
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  2. Mohsen Ashourian
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  3. Leila Ghabeli
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Correspondence to Mohsen Ashourian.

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Arshaghi, A., Ashourian, M. & Ghabeli, L. Feature selection based on buzzard optimization algorithm for potato surface defects detection. Multimed Tools Appl 79, 26623–26641 (2020). https://doi.org/10.1007/s11042-020-09236-3

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