A Hybrid Classification Method Based on Machine Learning Classifiers to Predict Performance in Educational Data Mining

Abstract

Machine learning algorithm can be applied in education data mining (EDM) to extract knowledge. Educational data mining is an important practice of automatic extraction and segmentation of useful information from the education data sources. This paper is focused on comparison and study of hybrid model of classification and machine learning algorithms based on decision tree, clustering, artificial neural network, Naïve Bayes, etc. This paper introduces concepts of popular algorithm for new researchers of this area. The paper discusses hybrid classification model using machine learning algorithms using voting that can be used to analyze the performance of students. We have used open source data mining tool Weka for a practical experiment on data set of students that serve the purpose of prediction, classification, visualization, etc. The findings of this paper reveal that hybrid method of classification are more efficient for prediction of student-related data.