A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science

Abstract

Machine learning is as growing as fast as concepts such as Big data and the field of data science in general. The purpose of the systematic review was to analyze scholarly articles that were published between 2015 and 2018 addressing or implementing supervised and unsupervised machine learning techniques in different problem-solving paradigms. Using the elements of PRISMA, the review process identified 84 scholarly articles that had been published in different journals. Of the 84 articles, 6 were published before 2015 despite their metadata indicating that they were published in 2015. The existence of the six articles in the final papers was attributed to errors in indexing. Nonetheless, from the reviewed papers, decision tree, support vector machine, and Naïve Bayes algorithms appeared to be the most cited, discussed, and implemented supervised learners. Conversely, k-means, hierarchical clustering, and principal component analysis also emerged as the commonly used unsupervised learners. The review also revealed other commonly used algorithms that include ensembles and reinforce learners, and future systematic reviews can focus on them because of the developments that machine learning and data science is undergoing at the moment.