Classification of students by using an incremental ensemble of classifiers
Roshani Raut, Prashant R. Deshmukh · 2014
The amount of students data in the education system databases is growing day by day, so the knowledge taken out from these data need to be updated continuously. The training set of the supervised learning algorithms contains student's score in the test. Incremental learning ability is further significant for machine learning methodologies as student's data and the information is increasing. Against to the classical batch learning algorithm, incremental learning algorithm tries to forget unrelated information while training new instances. Now a days, combination of a classifiers is a novel concept for overall progress in the classification result. Therefore, an incremental ensemble of two classifiers namely Naïve Bayes, K-Star using majority voting scheme is proposed. The large scale comparison of a proposed ensemble technique by using different voting scheme with the state-of the art algorithm on the student's data set has been done. The experimental results shown high accuracy for the proposed ensemble for the student's classification. High accuracy was also achieved for the majority voting scheme as compared to other voting scheme.