Supervised Machine Learning Techniques in Intelligent Network Handovers

Abstract

Supervised Machine Learning (SML) is a critical analysis of algorithms, which conform to exterior abounding instances that determine the overall hypotheses and future instance predictions. In intelligent systems, supervised categorization is a critical aspect of machine learning. This article comprehends on fundamental SML techniques, through the comparison of different SML algorithms and determination of the most crucial supervised classification algorithm. These techniques: Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), JRip, Neural Networks, and the Decision tree, through the application of the Waikato Environmental of Knowledge Networks (WEKA) as a machine learning application. Considering the implementation of algorithm, the dataset on Diabetes was utilized during the classification process considering 789 instances composing of 8 attributes as a dependent variable and another as an independent variable in the analysis. Considering the discussion and the results, it was evident that SVM is precise and accurate, termed as an algorithm (Zhang N, Int. J. Collab. Intell. 1:298, 2016). The Random Forest and Naïve Bayes categorizing algorithm was denoted to be precise subsequent to the SVM. This paper indicates that the timeframe utilized to formulate precision and model is a different factor from the mean absolute error and the kappa statistic. Resultantly, the machine learning algorithm necessitate accuracy, precision, and minimal error in obtaining predictive SML.