Fusion of Classifiers Using Learning Automata Algorithm

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

doi:10.22061/jecei.2024.10950.750

تعداد نشریات	11
تعداد شماره‌ها	230
تعداد مقالات	2,344
تعداد مشاهده مقاله	3,742,678
تعداد دریافت فایل اصل مقاله	2,740,430

	Fusion of Classifiers Using Learning Automata Algorithm
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 6، دوره 13، شماره 1، فروردین 2025، صفحه 65-80 اصل مقاله (1.9 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.10950.750
نویسندگان
S. Mahmoudikhah¹؛ S. H. Zahiri^* ¹؛ I. Behravan²
¹Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
²Department of Electrical Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 05 خرداد 1403، تاریخ بازنگری: 20 مرداد 1403، تاریخ پذیرش: 03 شهریور 1403
چکیده
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.
کلیدواژه‌ها
Sonar Data؛ Reinforcement Learning؛ Learning Automata؛ Data Classification؛ Analytical Parameters

مراجع
[1] X. Fan, L. Lu, P. Shi, X. Zhang, "A novel sonar target detection and classification algorithm," Multimedia Tools Appl.s, 81(7): 10091-10106, 2022. [2] J. U. ROBERT, Principles of underwater sound for engineers. MCGRAW-HILL., 1983. [3] S. J. Davey, M. G. Rutten, B. Cheung, "A comparison of detection performance for several track-before-detect algorithms," EURASIP J. Adv. Signal Process., 2008(1): 1-10, 2007. [4] M. Hassaballah, A. I. Awad, Deep learning in computer vision: principles and applications. CRC Press, 2020. [5] D. P. Williams, "Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks," in Proc. 2016 23rd International Conference on Pattern Recognition (ICPR): 2497-2502, 2016. [6] M. Valdenegro-Toro, "Best practices in convolutional networks for forward-looking sonar image recognition," in OCEANS 2017-Aberdeen: 1-9, 2017. [7] E. L. Ferguson, R. Ramakrishnan, S. B. Williams, C. T. Jin, "Convolutional neural networks for passive monitoring of a shallow water environment using a single sensor," in Proc. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 2657-2661, 2017. [8] G. Huo, Z. Wu, J. Li, "Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data," IEEE Access, 8(1): 47407-47418, 2020. [9] F. Mousavipour, M. R. Mosavi, "Sonar data classification using neural network trained by hybrid dragonfly and chimp optimization algorithms," Wireless Pers. Commun., 129(1): 191-208, 2023. [10] Q. Li, L. Song, Y. Zhang, "Multiple extended target tracking by truncated JPDA in a clutter environment," IET Signal Process., 15(3): 207-219, 2021. [11] E. Fan, W. Xie, J. Pei, K. Hu, X. Li, V. Podpečan, "Improved joint probabilistic data association (JPDA) filter using motion feature for multiple maneuvering targets in uncertain tracking situations," Information, 9(12): 322, 2018. [12] T. Li, J. Prieto, H. Fan, J. M. Corchado, "A robust multi-sensor PHD filter based on multi-sensor measurement clustering," IEEE Commun. Lett., 22(10): 2064-2067, 2018. [13] W. Xiong, X. Gu, Y. Cui, "Tracking and data association based on reinforcement learning," Electronics, 12(11): 2388, 2023. [14] L. Snidaro, J. Garcia-Herrera, J. Llinas, E. Blasch, "Context-enhanced information fusion," Boosting Real-World Performance with Domain Knowledge, 2016. [15] J. Raol, "Multi-Sensor Data Fusion with MATLAB. 2009," ed: CRC press. [16] T. M. Mitchell, "Does machine learning really work?," AI magazine, 18(3): 11-11, 1997. [17] P. Domingos, The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books, 2015. [18] A. K. Bathla, S. Bansal, M. Kumar, "OKC classifier: an efficient approach for classification of imbalanced dataset using hybrid methodology," Soft Comput., 26(21): 11497-11503, 2022. [19] K. Taunk, S. De, S. Verma, A. Swetapadma, "A brief review of nearest neighbor algorithm for learning and classification," in Proc. 2019 International Conference on Intelligent Computing and Control Systems (ICCS): 1255-1260, 2019. [20] D. Wettschereck, T. Dietterich, "Locally adaptive nearest neighbor algorithms," Adv. Neural Inf. Process. Syst., 6(1): 184-191, 1993. [21] M. Sharma, S. K. Sharma, "Generalized K-nearest neighbour algorithm-a predicting tool," Int. J. Adv. Res. Comput. Sci. Software Eng., 3(11): 1-4, 2013. [22] C. M. Bishop, Neural networks for pattern recognition. Oxford university press, 1995. [23] H. Taud, J. Mas, "Multilayer perceptron (MLP),” in Geomatic approaches for modeling land change scenarios, Lecture Notes in Geoinformation and Cartography, Mexico City, 451-455, 2018. [24] K. L. Du, M. N. Swamy, Neural networks and statistical learning. Springer Science & Business Media, 2013. [25] M. A. Thathachar, P. S. Sastry, Networks of learning automata: Techniques for online stochastic optimization. Springer Science & Business Media, 2003. [26] F. Hourfar, H. J. Bidgoly, B. Moshiri, K. Salahshoor, A. Elkamel, "A reinforcement learning approach for waterflooding optimization in petroleum reservoirs," Eng. Appl. Artif. Intell., 77(1): 98-116, 2019. [27] M. L. v. TSetlin, "Automaton theory and modeling of biological systems," 1973. [28] N. S. Shahraki, S. H. Zahiri, "DRLA: Dimensionality ranking in learning automata and its application on designing analog active filters," Knowledge-Based Syst., 219(1): 106886, 2021. [29] Q. Wu, H. Liao, "Function optimisation by learning automata," Inf. Sci., 220(1): 379-398, 2013. [30] B. N. K. Reddy, C. A. Vaithilingam, S. Kamalakkannan, "SONAR based under water mine detection using machine learning algorithms," in Proc. 4th International Conference on Innovative Practices in Technology and Management (ICIPTM): 1-4, 2024. [31] Q. Wang, S. Du, W. Zhang, F. Wang, "Active sonar target recognition method based on multi‐domain transformations and attention‐based fusion network," IET Radar Sonar Navig., 2024. [32] F. Ahmad, M. Z. Ansari, R. Anwar, B. Shahzad, A. Ikram, "Deep learning based classification of underwater acoustic signals," Procedia Comput. Sci., 235(1): 1115-1124, 2024. [33] H. Yang, Y. Huang, Y. Liu, "Spatial attention deep convolution neural network for call recognition of marine mammal," in Proc. International Conference on Autonomous Unmanned Systems: 2725-2733, 2022. [34] S. Z. Tian, D. B. Chen, Y. Fu, J. L. Zhou, "Joint learning model for underwater acoustic target recognition," Knowledge-Based Syst., 260(1): 110-119, 2023.
آمار تعداد مشاهده مقاله: 520 تعداد دریافت فایل اصل مقاله: 379

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

آمار

Fusion of Classifiers Using Learning Automata Algorithm