Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio

Yan Xia, Jian Shu, Na Xu, Hui Feng

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.

References

  1. Han, Wenyu, Mei, Shiwei, 2011. Master discipline law, Cultivate discipline culture, Promote development, China Higher Education, vol. 7.
  2. Hood, W.W, Wilson, C.S., 2001. The literature of bibliometrics, scientometrics, and informetrics, Scientometrics, vol. 52.
  3. Marijk, van der Wender, 2008. Ranking and Classification in Higher Education: A European Perspective, Higher Education, vol. 23.
  4. Jamil, Salmi, Alenoush, Saroyan, 2007. League Tables as Policy Instrument: Uses and Misuses, Higher Education Management and Policy(OECD), vol. 19.
  5. CDGDC, 2013. Brief introduction of discipline evaluation, http://www.chinadegrees.cn/xwyyjsjyxx/xxsbdxz/276 985.shtml.
  6. Moed, H. F., 2006. Bibliometric Rankings of World Universities, The Netherlands: Centre for Science and Technology Studies (CWTS), Leiden University.
  7. Aghion P, Dewatripont M, Hoxby C, 2010. The governance and performance of universities: evidence from Europe and the US The governance and performance of universities: evidence from Europe and the US, Economic Policy, vol. 25.
  8. Han, Jiawei, Kamber, Micheling, Pei, Jian, 2011. The book, Data Mining: Concepts and Techniques, 3rd edition.
  9. Quinlan, J. R., 1987. Simplifying decision trees, International Journal of Man-Machine Studies, vol. 27.
  10. CDGDC, 2012. Discipline Evaluation Indicator System in 2012, http://www.chinadegrees.cn/xwyyjsjyxx/xxsb dxz/277134.shtml.
  11. Yang, Xue, Feng, Hui, 2012. An Evaluation on the InputOutput Performance of Universities Based on Principal Component Analysis, Shanghai Management Science, vol. 34.
  12. Carlo, Batin, 2010. The book, Data Quality: Concepts, Methodologies and Techniques, 1st edition.
  13. Cai, Z, 2011. Identifying product failure rate based on a conditional Bayesian network classifier, Expert Systems with Applications, vol. 38.
  14. Shanghai Municipal Government, 2012. First-class Disciplines in Shanghai universities and colleges, http://www.shanghai.gov.cn/shanghai/node2314/node 2319/node12344/u26ai33230.html.
Download


Paper Citation


in Harvard Style

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 Education - Volume 2: CSEDU, ISBN 978-989-758-179-3, pages 77-84. DOI: 10.5220/0005748000770084


in Bibtex Style

@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 - Volume 2: CSEDU,},
year={2016},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005748000770084},
isbn={978-989-758-179-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Computer Supported Education - Volume 2: CSEDU,
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