Performance Accuracy of Classification Algorithms for Web Learning System

This Research paper focused on Classification accuracy based on Users ’ Preferences from the Web Learning System. This comparative study considers various classification algorithms like j48, Random Tree, Random Forest, CART and Naive Bayes in the Web Learning System. It also focuses on Artificial Neural Network (ANN) algorithms. The classification accuracy is identified by user’s requirements based on the cognitive input. In this research Neural Network approach like MLP, PMLP, GO PMLP and PSO PMLP algorithms are proposed and validated. These algorithms classify the user preferences of the Web Learning System. As the User Preferences have many potential applications, mining on the User Preferences of the Web Learning System users was contemplated. Based on the response of the current users, a decision tree induction algorithm is used to predict the requirements of future users