期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2017
卷号：48
期号：3
页码：128-138
DOI：10.14445/22312803/IJCTT-V48P126
出版社：Seventh Sense Research Group
摘要：Supervised Machine Learning (SML) is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. Supervised classification is one of the tasks most frequently carried out by the intelligent systems. This paper describes various Supervised Machine Learning (ML) classification techniques, compares various supervised learning algorithms as well as determines the most efficient classification algorithm based on the data set, the number of instances and variables (features).Seven different machine learning algorithms were consideredDecision Table, Random Forest (RF) , Naïve Bayes (NB) , Support Vector Machine (SVM), Neural Networks (Perceptron), JRip and Decision Tree (J48) using Waikato Environment for Knowledge Analysis (WEKA)machine learning tool.To implement the algorithms, Diabetes data set was used for the classification with 786 instances with eight attributes as independent variable and one as dependent variable for the analysis. The results show that SVMwas found to be the algorithm with most precision and accuracy. Naïve Bayes and Random Forest classification algorithms were found to be the next accurate after SVM accordingly. The research shows that time taken to build a model and precision (accuracy) is a factor on one hand; while kappa statistic and Mean Absolute Error (MAE) is another factor on the other hand. Therefore, ML algorithms requires precision, accuracy and minimum error to have supervised predictive machine learning.
关键词：Machine Learning; Classifiers; Data Mining Techniques; Data Analysis; Learning Algorithms; Supervised Machine Learning
