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

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Safira Begum, Sunita S Padmannavar


MLA Style: Safira Begum, and 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, Apr. 2022, pp. 223-235. Crossref,

APA Style: Safira Begum, & Sunita S Padmannavar. (2022). Prediction of Student Performance using Genetically Optimized Feature Selection with Multiclass Classification. International Journal of Engineering Trends and Technology, 70(4), 223-235.

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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