An Heuristic Feature Selection Algorithm to Evaluate Academic Performance of Students
- Resource Type
- Conference
- Authors
- Ajibade, Samuel-Soma M.; Ahmad, Nor Bahiah; Shamsuddin, Siti Mariyam
- Source
- 2019 IEEE 10th Control and System Graduate Research Colloquium (ICSGRC) Control and System Graduate Research Colloquium (ICSGRC), 2019 IEEE 10th. :110-114 Aug, 2019
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Classification algorithms
Feature extraction
Prediction algorithms
Performance evaluation
Heuristic algorithms
Control systems
Data mining
educational data mining
differential evolution
student performance
feature selection algorithm
classification algorithm
- Language
The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student’s performances. Feature Selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues.