Improvement of the Accuracy of Prediction Using Unsupervised Discretization Method: Educational Data Set Case Study

This paper presents a comparison of the efficacy of unsupervised and supervised discretization methods for educational data from blended learning environment. Naïve Bayes classifier was trained for each discretized data set and comparative analysis of prediction models was conducted. The research goal was to transform numeric features into maximum independent discrete values with minimum loss of information and reduction of classification error. Proposed unsupervised discretization method was based on the histogram distribution and implementation of oversampling technique. The main contribution of this research is improvement of accuracy prediction using the unsupervised discretization method which reduces the effect of ignoring class feature for educational data set