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A New Enhanced Learning Approach to Automatic Image Classification Based on Salp Swarm Algorithm

Mohammad Behrouzian Nejad1, Mohammad Ebrahim Shiri1,2,*

1 Department of computer Engineering, Borujerd Branch, Islamic Azad University, Borujerd, Iran
2 Department of Mathematics and Computer Science, Amirkabir University, Tehran, Iran

* Corresponding Author: E-mail: email

Computer Systems Science and Engineering 2019, 34(2), 91-100.


In this paper we propose a new image classification technique. According to this note that most research focuses on extraction of features in the frequency domain, location, and reduction of feature dimensions, in this research we focused on learning step in image classification. The main aim is to use the heuristic methods to increase the function of the estimator of the learning algorithm and continue to achieve the desired state, as well as categorization without user interference and automatically performed by the model produced from the above steps. So, in this paper, a new learning approach based on the Salp Swarm Algorithm was proposed that was implemented and evaluated on learning algorithm Decision Tree, K-Nearest Neighbors and Naïve Bayes. The results demonstrate the improvement of the performance of learning algorithms in all the achieved criteria by using the SSA algorithm in comparison with traditional learning algorithms. In the accuracy, sensitivity, classification error and F1 criterion, the best performance of the proposed model is using the Decision Tree learning method with values of 99.17%, 100%, 0.83% and 95.65% respectively. In the specificity and precision criterion, the best performance of the proposed model is based on K-Nearest Neighbors learning method with values of 100%.


Cite This Article

M. Behrouzian Nejad and M. Ebrahim Shiri, "A new enhanced learning approach to automatic image classification based on salp swarm algorithm," Computer Systems Science and Engineering, vol. 34, no.2, pp. 91–100, 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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