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Authors: Yan Xia 1 ; Jian Shu 2 ; Na Xu 3 and Hui Feng 1

Affiliations: 1 Shanghai Education Evaluation Institute, China ; 2 Shanghai General Motor, China ; 3 Shanghai Municipal Education Examinations, China

ISBN: 978-989-758-179-3

Keyword(s): Data Mining, Information Entropy, Information Gain Ratio, Decision Tree, Discipline Classification, Discipline Evaluation.

Related Ontology Subjects/Areas/Topics: Assessment and Accreditation of Courses and Institutions ; Computer-Supported Education ; Learning/Teaching Methodologies and Assessment ; Social Context and Learning Environments

Abstract: Discipline evaluation is an important part in higher education evaluation. It plays a significant role in discipline construction in universities and colleges. It is challenging how to use scientific discipline evaluation to classify disciplines, such as advantageous disciplines and newly-emerging ones. This paper proposes an algorithm of discipline decision tree classification based on weighted information gain ratio. It determines evaluation attributes and creates decision tree according to weighted information gain ratio. Discipline classification rules are deduced by decision tree. An automatic classification system is developed, implementing the algorithm and analysing data from universities and colleges in Shanghai. Experimental results show that our scheme can achieve about 83.33% accuracy in forecasts. It provides advice and guidance for discipline evaluation, and establishes foundation for discipline development strategy.

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Paper citation in several formats:
Xia Y., Shu J., Xu N. and Feng H. (2016). Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio.In Proceedings of the 8th International Conference on Computer Supported EducationISBN 978-989-758-179-3, pages 77-84. DOI: 10.5220/0005748000770084

@conference{csedu16,
author={Yan Xia and Jian Shu and Na Xu and Hui Feng},
title={Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio},
booktitle={Proceedings of the 8th International Conference on Computer Supported Education},
year={2016},
pages={77-84},
doi={10.5220/0005748000770084},
isbn={978-989-758-179-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Computer Supported Education
TI - Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio
SN - 978-989-758-179-3
AU - Xia Y.
AU - Shu J.
AU - Xu N.
AU - Feng H.
PY - 2016
SP - 77
EP - 84
DO - 10.5220/0005748000770084

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