Title: Optimising feature selection in educational datasets using an enhanced teaching-learning-based optimisation algorithm

Authors: D.I. George Amalarethinam; A. Emima

Addresses: PG and Research Department of Computer Science, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli-620020, Tamil Nadu, India ' PG and Research Department of Computer Science, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli-620020, Tamil Nadu, India

Abstract: Educational data mining (EDM) is an emerging study topic that helps schools improve student performance. Selecting only relevant data reduces model input parameters with feature selection. It reduces dimensionality by selecting a subset of features and removing incorrect, superfluous, or noisy ones. It improves learning accuracy, computational cost, and model interpretability. This impacts the accuracy of performance models used to assess student outcomes. Most optimisation methods, including the genetic algorithm, must optimise many governing parameters for greater performance. Optimisation approaches using wrapper feature selection (WFS) improve classifier prediction. The proposed ETLBO algorithm with WFS techniques uses the Euclidean distance formula to assess fitness value and popular control parameters to select the optimal feature subset. The algorithm above is used on the educational dataset. Classification algorithms evaluate the best features from TLBO ETLBO algorithms: four algorithms classify performance metrics: GNB, LR, SVM, and K-nearest neighbour. Experimental results suggest that the ELTBO algorithm's best feature subset improves classification accuracy for GNB, LR, SVM, and KNN compared to TLBO.

Keywords: classification algorithms; feature selection; FS; optimisation technique; Euclidean distance; enhanced teacher learner based optimisation; ETLBO; teacher learner based optimisation; TLBO; Guassian Naive Bayes; GNB; logistic regression; LR.

DOI: 10.1504/IJRIS.2026.153554

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.3, pp.131 - 142

Received: 11 Jul 2024
Accepted: 27 Sep 2024
Published online: 14 May 2026 *

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