A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification

Abstract

Machine Learning (ML) is a kind of Artificial Intelligence (AI) technique which allows the system to obtain knowledge with no explicit programming. The main intention of ML technique is to enable the computers to learn with no human assistance. ML is mainly divided into three categories namely supervised, unsupervised and semi-supervised learning approaches. Supervised algorithms need humans to give input and required output, in addition to providing feedback about the prediction accuracy in the training process. Unsupervised learning approaches are contrast to supervised learning approaches where it does not require any training process. But, supervised learning approaches are simpler than unsupervised learning approaches. This paper reviews the supervised learning approaches which are widely used in data classification process. The techniques are reviewed on the basis of aim, methodology, advantages and disadvantages. Finally, the readers can get an overview of supervised ML approaches in terms of data classification.