A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine

Abstract

Machine Learning Algorithms are employed in characterization, pattern recognition, and prediction. A hybrid model helps in reducing the computational complexity, improves accuracy, and results in an effective method for classification. The misclassification of the individual classifier is often excluded in a hybrid classifier. The objective of this research was to develop a hybrid classification model of Artificial Neural Network and non-linear kernel Support Vector Machine as an intelligent tool for achieving better classification performance and minimizing error rates. This study further evaluated the irreducibility and identifiability statistical properties of the ANN-SVM model. To achieve the hybridization of ANN and SVM, the research first obtained weights from the fitted Support Vector Machine model, and these weights were used as the initial weights in the Artificial Neural Network structure. The experiment was carried out in three distinct phases: selection of input features using the Boruta Wrapper Algorithm, classifier learning, and classifier combined effect and classification optimization. The study findings suggest that the hybrid ANN-SVM approach gives a higher performance accuracy of 89.7% and is more precise as compared to single ANN, SVM data mining algorithms. Therefore, the hybrid of ANN-SVM is the best binary classification system for classifying diabetes mellitus. The statistical software used for analysis was R.