Fusion of Classifiers Using Learning Automata Algorithm

Mahmoudikhah, S.; Zahiri, S. H.; Behravan, I.

doi:10.22061/jecei.2024.10950.750

Articles in Press

Document Type : Original Research Paper

Authors

¹ Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

² Department of Electrical Engineering, University of Birjand, Birjand, Iran.

https://doi.org/10.22061/jecei.2024.10950.750

Abstract

Background and Objectives: Sonar data processing is used to identify and track targets whose echoes are unsteady. So that they aren’t trusty identified in typical tracking methods. Recently, RLA have effectively cured the accuracy of undersea objective detection compared to conventional sonar objective cognition procedures, which have robustness and low accuracy.
Methods: In this research, a combination of classifiers has been used to improve the accuracy of sonar data classification in complex problems such as identifying marine targets. These classifiers each form their pattern on the data and store a model. Finally, a weighted vote is performed by the LA algorithm among these classifiers, and the classifier that gets the most votes is the classifier that has had the greatest impact on improving performance parameters.
Results: The results of SVM, RF, DT, XGboost, ensemble method, R-EFMD, T-EFMD, R-LFMD, T-LFMD, ANN, CNN, TIFR-DCNN+SA, and joint models have been compared with the proposed model. Considering that the objectives and databases are different, we benchmarked the average detection rate. In this comparison, Precision, Recall, F1_Score, and Accuracy parameters have been considered and investigated in order to show the superior performance of the proposed method with other methods.
Conclusion: The results obtained with the analytical parameters of Precision, Recall, F1_Score, and Accuracy compared to the latest similar research have been examined and compared, and the values are 87.71%, 88.53%, 87.8%, and 87.4% respectively for each of These parameters are obtained in the proposed method.

Keywords

Main Subjects

Data Fusion

Open Access

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Journal of Electrical and Computer Engineering Innovations (JECEI)

Fusion of Classifiers Using Learning Automata Algorithm

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References

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Articles in Press, Accepted Manuscript
Available Online from 01 September 2024

Fusion of Classifiers Using Learning Automata Algorithm

References

References

Send comment about this article

Articles in Press, Accepted Manuscript Available Online from 01 September 2024

Articles in Press, Accepted Manuscript
Available Online from 01 September 2024