Machine Learning | IJACSA

Machine learning is a branch of artificial intelligence in which systems improve their performance on a task by learning patterns from data rather than following explicitly programmed rules. It is broadly divided into supervised learning for classification and regression from labeled examples, unsupervised learning for clustering and dimensionality reduction on unlabeled data, and reinforcement learning for learning optimal actions through trial-and-error interaction with an environment. Common algorithms include decision trees, support vector machines, ensemble methods such as random forests and gradient boosting, and neural networks, chosen based on data characteristics, interpretability needs, and computational constraints. Industry surveys suggest most organizations remain in experimentation or pilot phases with machine learning, with only about a third reporting they have begun scaling programs organization-wide. Machine learning underlies applications across nearly every domain, including predictive maintenance, credit scoring, medical diagnosis support, and recommendation systems. As an open-access machine learning journal (an ML journal), IJACSA publishes comparative studies and applied research spanning these algorithm families.

Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed October 2026