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Applying Attribute Selection Algorithms in Academic Performance Prediction

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Book cover International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018 (ICICI 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 26))

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Abstract

There is an essential need to identify effective and efficient attribute selection algorithms for developing predictive and descriptive classifiers to predict students’ academic performance. Personal, Academic and Socio-economic data were collected from 100 MCA students through questionnaire and cleansed. Eight attribute selection algorithms available in WEKA were applied to identify the best attributes. These attribute sets were then employed by four rule-based classifiers and four tree-based classifiers to examine the rate of accuracy, recall and AUC. The results proved that dimension reduction improves classifier performance by a maximum margin of 9% in accuracy, 25% in recall and 45% in AUC, when attributes were selected by subset-based algorithms. Both academic and socio-economic data are required to develop effective classifiers.

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Authors and Affiliations

  1. School of Computer Science and Applications, REVA University, Bengaluru, India

    Mu Lin Wong & S. Senthil

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  1. Mu Lin Wong
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  2. S. Senthil
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Corresponding author

Correspondence to Mu Lin Wong .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, India

    Jude Hemanth

  2. Department of Electrical and Computer Engineering, Ryerson Communications Lab, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. Faculty of Engineering, Department of Telecommunication Engineering, Czech Technical University, Prague, Czech Republic

    Pavel Lafata

  4. School of Science, Joondalup Campus, Edith Cowan University, Joondalup, WA, Australia

    Zubair Baig

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Wong, M.L., Senthil, S. (2019). Applying Attribute Selection Algorithms in Academic Performance Prediction. In: Hemanth, J., Fernando, X., Lafata, P., Baig, Z. (eds) International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. ICICI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-03146-6_78

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