A Hybrid Feature Selection Framework for Predicting Students Performance

作     者：Maryam Zaffar Manzoor Ahmed Hashmani Raja Habib KS Quraishi Muhammad Irfan Samar Alqhtani Mohammed Hamdi 

作者机构：High Performance Cloud Computing(HPC3)Department of Computer and Information SciencesUniversiti Teknologi PETRONAS32610SeriIskandarMalaysia Department of Computer Science and Information TechnologyUniversity of LahorePakistan Department of Process EngineeringPakistan Institute of Engineering&Applied SciencesNiloreIslamabadPakistan Electrical Engineering DepartmentCollege of EngineeringNajran UniversityNajran61441Saudi Arabia College of Computer Science and Information SystemsNajran University61441NajranSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第1期

页      面：1893-1920页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

主　　题：Educational data mining feature selection hybrid feature selection 

摘      要：Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of *** selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic *** are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal *** this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of *** main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic *** key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based ***,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity *** proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of *** validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and *** reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better tha