Advancing Machine Learning: Development, Evaluation, and Feature Engineering in Domain-Specific Applications

Abstract

The rapid advancements in machine learning and the increasing availability of extensive datasets have significantly propelled the field of image classification. This study presents a comprehensive evaluation of three prominent machine learning models—Convolutional Neural Networks (CNNs), k-Nearest Neighbors (kNN), and Random Forest classifiers—on a specific image classification task. The research investigates the efficacy of these models through various performance metrics, examining their strengths and limitations. CNNs demonstrated superior accuracy and robustness, attributed to their ability to learn hierarchical features directly from image data. However, they require substantial computational resources and large datasets. The kNN classifier, while straightforward and easy to implement, exhibited limitations in handling high-dimensional data. The Random Forest classifier showed promise in structured data analysis but required effective feature engineering to enhance its performance with image data. The study also highlights the critical role of feature engineering techniques, data preprocessing, and hyperparameter tuning in optimizing model performance. Advanced CNN architectures, ensemble methods, and real-time deployment strategies are proposed as future research directions to further enhance image classification systems. This research provides valuable insights for developing more accurate and efficient image classification models, with potential applications across various domains..