In-depth Analysis on Machine Learning Approaches

Machine learning (ML) approaches cover several aspects of daily life tasks, including knowledge representation, data analysis, regression, classification, recognition, clustering, planning, reasoning, text recommendation, and perception. The ML approaches enable applications to learn and adapt with or without being directly programmed from previous data or experience. The ML techniques, coupled with current technologies, provide a range of solutions, starts from vision-based applications to text-generation applications. To this end, this article presents a comprehensive overview of the approaches of ML, including supervised, unsupervised, semi-supervised, reinforcement, and self-learning. This review critically examines the roles performed by these aforementioned approaches in terms of their weaknesses and strengths. Furthermore, within this study, a new comparative analysis is conducted by reviewing existing studies and evaluating ML techniques using metrics including data requirement, accuracy, complexity, interpretability, scalability, applications, and challenges. Thereafter, the implemented ML techniques are classified, and their key findings are examined. The comprehensive review demonstrates that neither standalone nor hybrid ML techniques can completely satisfy all of the evaluated metrics, the necessity of customized solutions based on the requirements of particular applications.