Efficient knowledge transformation system using pair of classifiers for prediction of students career choice

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Abstract

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 in psychometric test can form the training set for the system which helps the students to choose the right career. 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. Effective knowledge transformation system can be build using different pair of classifiers for the purpose of prediction of student's career choice. In this paper, four pair of classifier are used.

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APA

Ade, R., & Deshmukh, P. R. (2015). Efficient knowledge transformation system using pair of classifiers for prediction of students career choice. In Procedia Computer Science (Vol. 46, pp. 176–183). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.02.009

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