An incremental ensemble of classifiers as a technique for prediction of student's career choice
Roshani Raut, Prashant R. Deshmukh · 2014
The ability to predict the career of students can be beneficial in a huge number of different techniques which are connected with the education structure. Student's marks and the result of some kind of psychometric test on students can form the training set for the supervised data mining algorithms. As the student's data in the educational systems is increasing day by day, the incremental learning properties are important for machine learning research. Against to the classical batch learning algorithm, incremental learning algorithm tries to forget unrelated information while training new instances. These days, combining classifiers is nothing but taking more than one opinion contributes a lot, to get more accurate results. Therefore, a suggestion is an incremental ensemble of three classifiers namely Naïve Bayes, K-Star, SVM using voting scheme. The ensemble technique proposed in this paper is compared with the incremental algorithms, without any ensemble concept, for the student's data set and it was found that the proposed algorithm gives better accuracy.