Classification of galaxies from image features using best parameter selection by horse herd optimization algorithm (HOA)

With the advancement of observation technology, visual data has made significant progress, rendering manual image classification less effective. Consequently, various image processing and automatic classification methods have garnered attention from researchers. Scientists estimate that there are approximately 2 trillion observable galaxies in the universe. Each galaxy possesses unique characteristics that are distinguishable. Therefore, finding a method to quickly and accurately identify these characteristics of each galaxy and classify them rapidly can greatly enhance the galaxy detection and classification process, while minimizing human errors. The objective of the present study is to determine the class of galaxies with from telescope image features using an optimized classifier with best parameters. The proposed method uses the HOA algorithm, based on the behavior of horse herds, to find the best parameters. This method evaluates the model's error with different SVM parameters and selects the optimal SVM parameters for constructing M-SVM. Using this method, the proposed algorithm is trained and ultimately applied to classify the test data. The results indicate that the developed model correctly classified up to 94.11% of the test dataset (1) and 90.74% of the test dataset (2).