Feature selection is critical in fields like data mining and pattern classification, as it eliminates irrelevant data and enhances the quality of highly dimensional datasets. This study explores the effectiveness of the Q-learning embedded sine cosine algorithm (QLESCA) for feature selection in industrial casting defect detection using the VGG19 model. QLESCA’s performance is compared to other optimization algorithms, with experimental results showing that QLESCA outperforms the other algorithms in terms of classification metrics. The best accuracy achieved by QLESCA is 97.0359%, with an average fitness value of − 0.99124. The proposed method provides a promising approach to improve the accuracy and reliability of industrial casting defect detection systems, which is essential for product quality and safety. Our findings suggest that using powerful optimization algorithms like QLESCA is crucial for obtaining the best subsets of information in feature selection and achieving optimal performance in classification tasks.
Optimizing Feature Selection for Industrial Casting Defect Detection Using QLESCA Optimizer
Lect. Notes Electrical Eng.
International Conference on Robotics, Vision, Signal Processing and Power Applications ; 2021 April 05, 2021 - April 06, 2021
Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications ; Chapter : 61 ; 485-491
2024-03-31
7 pages
Article/Chapter (Book)
Electronic Resource
English
Machine learning , Support vector machine (SVM) classifier , Convolutional neural networks , Swarm intelligence , Feature engineering Engineering , Control, Robotics, Mechatronics , Signal, Image and Speech Processing , Power Electronics, Electrical Machines and Networks , Artificial Intelligence , Computational Intelligence
Filter-based feature selection for rail defect detection
British Library Online Contents | 2004
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