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DOI: 10.14569/IJACSA.2012.031003
PDF

A Decision Tree Classification Model for University Admission System

Author 1: Abdul Fattah Mashat
Author 2: Mohammed M. Fouad
Author 3: Philip S. Yu
Author 4: Tarek F. Gharib

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 10, 2012.

  • Abstract and Keywords
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Abstract: Data mining is the science and techniques used to analyze data to discover and extract previously unknown patterns. It is also considered a main part of the process of knowledge discovery in databases (KDD). In this paper, we introduce a supervised learning technique of building a decision tree for King Abdulaziz University (KAU) admission system. The main objective is to build an efficient classification model with high recall under moderate precision to improve the efficiency and effectiveness of the admission process. We used ID3 algorithm for decision tree construction and the final model is evaluated using the common evaluation methods. This model provides an analytical view of the university admission system.

Keywords: Data Mining; Supervised Learning; Decision Tree; University Admission System; Model Evaluation.

Abdul Fattah Mashat, Mohammed M. Fouad, Philip S. Yu and Tarek F. Gharib, “A Decision Tree Classification Model for University Admission System” International Journal of Advanced Computer Science and Applications(IJACSA), 3(10), 2012. http://dx.doi.org/10.14569/IJACSA.2012.031003

@article{Mashat2012,
title = {A Decision Tree Classification Model for University Admission System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.031003},
url = {http://dx.doi.org/10.14569/IJACSA.2012.031003},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {10},
author = {Abdul Fattah Mashat and Mohammed M. Fouad and Philip S. Yu and Tarek F. Gharib}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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