Prediction of Student Performance using Genetically Optimized Feature Selection with Multiclass Classification

Prediction of Student Performance using Genetically Optimized Feature Selection with Multiclass Classification

© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Safira Begum, Sunita S Padmannavar
DOI :  10.14445/22315381/IJETT-V70I4P219

How to Cite?

Safira Begum, Sunita S Padmannavar, "Prediction of Student Performance using Genetically Optimized Feature Selection with Multiclass Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 223-235, 2022. Crossref,

Educational data mining is the key aspect of improving students` performance in education. the academic performance of students or instructors can be predicted by using the techniques and algorithms in educational data mining and data mining. the paper proposed a machine learning approach to predict the academic performance of secondary school students in Mathematics and Portuguese lessons. the proposed algorithm primarily applies the normalization and z-score normalization in the pre-processing stage to solve the unbalanced class distribution problem. Then, feature selection processes are performed using a Genetic algorithm. Students` success in Mathematics and Portuguese lessons is estimated by the k-nearest neighbour (KNN), linear discriminant analysis (LDA) and support vector machine (SVM) classifications. the experimental results compare the accuracy, precision, F-score, and sensitivity values of the abovementioned methods.

Educational Data Mining, K-Nearest Neighbour, Linear Discriminant Analysis, Machine Learning, Support Vector Machine.

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