Universiti Teknologi Malaysia Institutional Repository

Genetic algorithm based feature selection for predicting student’s academic performance

Al Farissi, Al Farissi and Mohamed Dahlan, Halina and Samsuryadi, Samsuryadi (2020) Genetic algorithm based feature selection for predicting student’s academic performance. In: 4th International Conference of Reliable Information and Communication Technology, IRICT 2019, 22 - 23 September 2019, Johor Bahru, Malaysia.

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Official URL: http://dx.doi.org/10.1007/978-3-030-33582-3_11


Recently, student’s academic performance prediction has become an increasingly prominent research topic in the field of Educational Data Mining (EDM). The prediction of student’s academic performance aims to explore information that is beneficial to the learning process of student. Therefore, accurate prediction of student’s academic performance provide benefits for education institutions to improve the quality of their institutions by improving the learning process of students. In predicting the student’s academic performance, the problem of high dimensional dataset is often faced in the datasets which significantly impacts the accuracy of student academic performance prediction. This paper proposed Genetic Algorithm based Feature Selection (GAFS) along with selected single classifier for classification in order to improve the accuracy in predicting student academic performance. Kaggle dataset is used in this paper and two phase of experiment have been conducted, single classifier without GAFS, and single classifier with GAFS. Results from the experiments show that, the accuracy of the proposed GAFS for classification makes an impressive performance in predicting student academic performance in terms of accuracy compare to existing techniques.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Classification, Feature selection
Subjects:H Social Sciences > HF Commerce
Divisions:International Business School
ID Code:92442
Deposited By: Widya Wahid
Deposited On:28 Sep 2021 15:44
Last Modified:28 Sep 2021 15:44

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