Handling High Dimensional Educational Data using Feature Selection Techniques


  • Amirah Mohamed Shahiri School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
  • Wahidah Husain School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
  • Nur’Aini Abd Rashid Department of Computer Sciences, College of Computer & Information Sciences, Princess Nourah bint Abdulrahman University, KSA


Educational Data Mining (EDM), Feature Selection, Filter, High Dimensional Data, , Wrapper


Huge amounts of data in educational datasets may cause the problem in producing quality data. Recently, data mining approach are increasingly used by educational data mining researchers for analyzing the data patterns. However, many research studies have concentrated on selecting suitable learning algorithms instead of performing feature selection process. As a result, these data has problem with computational complexity and spend longer computational time for classification. The main objective of this research is to provide an overview of feature selection techniques that have been used to analyze the most significant features. Then, this research will propose a framework to improve the quality of students’ dataset. The proposed framework uses filter and wrapper based technique to support prediction process in future study.


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How to Cite

Mohamed Shahiri, A., Husain, W., & Abd Rashid, N. (2017). Handling High Dimensional Educational Data using Feature Selection Techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-12), 89–93. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2775