A systematic review of the machine learning algorithms for the computational analysis in different domains

Abstract

In this paper machine learning algorithms have been discussed and analyzed. It has been discussed considering computational aspects in different domains. These algorithms have the capability of building mathematical and analytical model. These models may be helpful in the decision-making process. This paper elaborates the computational analysis in three different ways. The background and analytical aspect have been presented with the learning application in the first phase. In the second phase detail literature has been explored along with the pros and cons of the applied techniques in different domains. Based on the literatures, gap identification and the limitations have been discussed and highlighted in the third phase. Finally, computational analysis has been presented along with the machine learning results in terms of accuracy. The results mainly focus on the exploratory data analysis, domain applicability and the predictive problems. Our systematic analysis shows that the applicability of machine learning is wide and the results may be improved based on these algorithms. It is also inferred from the literature analysis that at the applicability of machine learning algorithm has the capability in the performance improvement. The main methods discussed here are classification and regression trees (CART), logistic regression, naïve Bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM) and decision tree (DT). The domain covered mainly are disease detection, business intelligence, industry automation and sentiment analysis.