Using Multi-objective Meta-heuristics For Data Mining

Résumé: The ability to extract knowledge from large datasets is essential for innovation and informed decision-making, a process known as knowledge extraction or data mining. Traditional methods often fall short in fully utilizing data potential, necessitating the development of new algorithms for better insights. This thesis explores an innovative approach by integrating deep learning with advanced feature selection techniques to improve the classification accuracy of COVID-19 cases from chest X-ray images. The dataset includes X-ray images categorized as COVID-19, pneumonia, and normal. We employ the Binary Multi-Objective Henry Gas Solubility Optimization Algorithm (B-MOHGSO) for feature selection and leverage models like AlexNet, VGG19, GoogleNet, and ResNet for feature extraction. Eight versions of B-MOHGSO were tested, with k-nearest neighbors (k-NN) as the classifier. The study highlights the significant impact of S-shaped and V-shaped transfer functions on binary transformations and classifier performance in high-dimensional medical imaging. Notably, B-MOHGSO algorithms, particularly those using V-shaped transfer functions, excelled in selecting relevant features while maintaining high accuracy. When combined with the VGG19 model and SVM classifier, B-MOHGSO significantly reduced the feature set without sacrificing performance. The application of B-MOHGSO in COVID-19 classification is crucial for identifying key features that enhance diagnostic processes and treatment strategies. By adapting MOHGSO for discrete optimization, this research aims to address the complexities of high-dimensional medical data and improve healthcare analytics outcomes.

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