Handling High Dimensional Educational Data using Feature Selection Techniques

Authors

  • 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

Keywords:

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

Abstract

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.

References

A. Peña-Ayala, “Educational data mining: A survey and a data mining-based analysis of recent works,” Expert Syst. Appl., vol. 41, no. 4 PART 1, pp. 1432–1462, Mar. 2014.

C. Romero and S. Ventura, “Educational Data Mining: A Review of the State of the Art,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 40, no. 6, pp. 601–618, Nov. 2010.

A. Acharya and D. Sinha, “Application of Feature Selection Methods in Educational Data Mining,” Int. J. Comput. Appl., vol. 103, no. 2, pp. 34–38, 2014.

A. Bidgoli and M. N. Parsa, “A Hybrid Feature Selection by Resampling , Chi squared and Consistency Evaluation Techniques,” Eng. Technol., vol. 6, no. 8, pp. 276–285, 2012.

B. Singh, N. Kushwaha, and O. P. Vyas, “A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty,” J. Data Anal. Inf. Process., no. November, pp. 95–105, 2014.

M. Ramaswami and R. Bhaskaran, “A Study on Feature Selection Techniques in Educational Data Mining,” J. Comput., vol. 1, pp. 7–11, 2009.

S. T. Jishan, R. I. Rashu, N. Haque, and R. M. Rahman, “Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique,” Decis. Anal., vol. 2, no. 1, pp. 1–25, 2015.

J. Tang, S. Alelyani, and H. Liu, “Feature Selection for Classification: A Review,” Data Classif. Algorithms Appl., pp. 37–64, 2014.

H. Liu and L. Yu, “Toward integrating feature selection algorithms for classification and clustering,” Knowl. Data Eng. IEEE Trans., vol. 17, no. 4, pp. 491–502, 2005.

V. Kumar, “Feature Selection: A literature Review,” Smart Comput. Rev., vol. 4, no. 3, 2014.

S. Beniwal and J. Arora, “Classification and Feature Selection Techniques in Data Mining,” Int. J. Eng. Res. Technol. data Min., vol. 1, no. 6, pp. 1–6, 2012.

Ö. Uncu and I. B. Türkşen, “A novel feature selection approach: Combining feature wrappers and filters,” Inf. Sci. (Ny)., vol. 177, no. 2, pp. 449–466, 2007.

M. Doshi and S. K. Chaturvedi, “Correlation Based Feature Selection ( Cfs ) Technique To Predict Student Perfromance,” Int. J. Comput. Networks Commun., vol. 6, no. 3, pp. 197–206, 2014.

H. M. Harb and M. A. Moustafa, “Selecting Optimal Subset of Features for Student Performance Model,” Int. J. Comput. Sci. Issues, vol. 9, no. 5, pp. 253–262, 2012.

P. Cortez and A. Silva, “Using Data Mining To Predict Secondary School Student Performance,” 5th Annu. Futur. Bus. Technol. Conf., vol. 2003, no. 2000, pp. 5–12, 2008.

K. Michalak and H. Kwasnicka, “Correlation-based Feature Selection Strategy in Neural Classification,” Sixth Int. Conf. Intell. Syst. Des. Appl., vol. 1, pp. 741–746, 2006.

A. G. Karegowda, A. S. Manjunath, and M. A. Jayaram, “Comparative Study of Attribute Selection Using Gain Ratio and Correlation Based Feature Selection,” Int. J. Inf. Technol. Knowl. Manag., vol. 2, no. 2, pp. 271–277, 2010.

S. Das, “Filters, wrappers and a boosting-based hybrid for feature selection,” Engineering, pp. 74–81, 2001.

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Published

2017-09-15

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

Issue

Vol. 9 No. 2-12: The Challenge of Computer and Engineering in Building Sustainable Service Systems II

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Articles

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