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Empirical study on the effect of using synthetic attributes on classification algorithms

Empirical study on the effect of using synthetic attributes on classification algorithms PurposeThe purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course.Design/methodology/approachThe paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines.FindingsThe outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable.Originality/valueThis paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

Empirical study on the effect of using synthetic attributes on classification algorithms

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References (31)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1756-378X
DOI
10.1108/IJICC-08-2016-0029
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course.Design/methodology/approachThe paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines.FindingsThe outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable.Originality/valueThis paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

Journal

International Journal of Intelligent Computing and Cybernetics – Emerald Publishing

Published: Jun 12, 2017

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