Optimized ensemble learning‐based student's performance prediction with weighted rough set theory enabled feature mining. (5th January 2023)

• Record Type: Journal Article
• Authors: Sateesh, Nayani; Srinivasa Rao, Pasupureddy; Rajya Lakshmi, Davuluri
• Is Part Of: Concurrency and computation. Volume 35:Number 7(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-05
• Subjects: feature mining -- optimization algorithms -- optimized ensemble learning -- student's performance prediction; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7601
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Currently, educational data mining act as a major part of student performance prediction approaches and their applications. However, more ensemble methods are needed to improve the student performance prediction, and also which helps increase the learning quality of the Student's performance. The usage of an ensemble classifier with rule mining to predict students' academic success is proposed. In response to this need, this research mainly concentrated on an ensemble classifier with rule mining to predict students' academic success. The feature mining is performed using the weighted Rough Set Theory method, in which the proposed meta‐heuristic algorithm optimizes the weight function. The variable optimization of the ensemble classifier is accomplished with the help of a combination of Harris Hawks Optimization (HHO), and Krill Herd Algorithm (KHA) known as Escape Energy Searched Krill Herd‐Harris Hawks Optimization (EES‐KHHO) for maximizing the prediction rate. Extensive tests are carried out on various datasets, and the findings show that our technique outperforms conventional approaches. Throughout the result analysis, the offered method attains a 92.77% accuracy rate, and also it attains a sensitivity rate of 94.87%. Therefore, the offered student performance prediction model achieves better effectiveness regarding various performance metrics.