NDLI: A Comparative Analysis of Classification Algorithms for Students College Enrollment Approval Using Data Mining

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A Comparative Analysis of Classification Algorithms for Students College Enrollment Approval Using Data Mining

A Comparative Analysis of Classification Algorithms for Students College Enrollment Approval Using Data Mining

Content Provider	ACM Digital Library
Author	Noaman, Amin Y. Ragab, Abdul Hamid M. Al-Ghamdi, Abdullah S. Madbouly, Ayman I.
Abstract	In big data universities, there may be several problems related to college admission and enrollment due to the increasing volume of students' data applying for higher education. So that there is a need to apply efficient data mining algorithms for better decision making for students' data classification. In this paper, nine classification algorithms are comparatively tested to find the optimum algorithm for students' dataset classification. The KAUODUS+ data base for the preparatory year students are used as an approval dataset for the experimental purposes. The Weka-knowledge analysis tool which is open source data mining workbench software is used for simulation of practical measurements. The classification technique that has the potential to significantly improve the performance is suggested for use in colleges' admission and enrollment applications. Impact of students GPA and their grades qualified materials with respect to their colleges' admission desire are visually analyzed. Results show that C4.5, PART and Random Forest algorithms give the highest performance and accuracy with lowest errors while IBK-E and IBK-M algorithms give high errors and low accuracy.
Starting Page	106
Ending Page	113
Page Count	8
File Format	PDF
ISBN	9781450330343
DOI	10.1145/2643604.2643631
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	College admission and enrollment Classification Machine learning Data mining Weka
Content Type	Text
Resource Type	Article

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