{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T16:12:27Z","timestamp":1758471147546,"version":"3.44.0"},"reference-count":0,"publisher":"Zarqa University","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IAJIT"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>This study addresses the need for a unified framework demonstrating Information Theory\u2019s (IT) pervasive impact across diverse Machine Learning (ML) tasks. We investigate how IT principles-including entropy, Mutual Information (MI), cross-entropy, KL-divergence, and Information Gain (IG)-rigorously guide ML model design, optimization, and interpretability. Our approach combines theoretical elucidation with empirical validation on standard benchmarks. IT enhances feature selection; for instance, MI-ranked features in the breast cancer dataset improved classifier accuracy to 95.1% (top 20) and 93% (top 5), outperforming F-score selection. It also improves model training; cross-entropy loss in Neural Networks (NNs) for Iris classification led to faster convergence and high accuracy (0.98 training, 0.95 validation), surpassing MSE loss. For generative models, KL-divergence effectively structures Variational Auto-Encoder (VAE) latent spaces from Modified National Institute of Standards and Technology (MNIST) data, promoting compact, continuous representations ideal for generation. Finally, the Information Bottleneck (IB) principle, applied to Canadian Institute For Advanced Research (CIFAR-100), yielded competitive test accuracy (51% vs. 50% for baseline Convolutional Neural Network. (CNN)) and reduced training time (925.02s vs. 1015.75s), highlighting its efficacy in learning compressed, predictive representations. These findings collectively underscore its continued crucial role as a unifying paradigm for addressing fundamental challenges in the evolving ML ecosystem, providing solutions for feature selection, model robustness, and generalization<\/jats:p>","DOI":"10.34028\/iajit\/22\/5\/1","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T09:28:54Z","timestamp":1756114134000},"source":"Crossref","is-referenced-by-count":0,"title":["Exploring the Intersection of Information Theory and Machine Learning"],"prefix":"10.34028","volume":"22","author":[{"given":"Yousef","family":"Jaradat","sequence":"first","affiliation":[]},{"given":"Mohammad","family":"Masoud","sequence":"additional","affiliation":[]},{"given":"Ahmad","family":"Manasrah","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Alia","sequence":"additional","affiliation":[]},{"given":"Khalid","family":"Suwais","sequence":"additional","affiliation":[]},{"given":"Sally","family":"Almanasra","sequence":"additional","affiliation":[]}],"member":"19944","published-online":{"date-parts":[[2025]]},"container-title":["The International Arab Journal of Information Technology"],"original-title":[],"language":"en","deposited":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T10:23:01Z","timestamp":1758450181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.iajit.org\/upload\/files\/Exploring-the-Intersection-of-Information-Theory-and-Machine-Learning.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.34028\/iajit\/22\/5\/1","archive":["Internet Archive"],"relation":{},"ISSN":["2309-4524","1683-3198"],"issn-type":[{"type":"electronic","value":"2309-4524"},{"type":"print","value":"1683-3198"}],"subject":[],"published":{"date-parts":[[2025]]}}}