Journal of Applied Mathematics

Decision Tree Classification Model for Popularity Forecast of Chinese Colleges

Xiangxiang Zeng, Sisi Yuan, You Li, and Quan Zou

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Abstract

Prospective students generally select their preferred college on the basis of popularity. Thus, this study uses survey data to build decision tree models for forecasting the popularity of a number of Chinese colleges in each district. We first extract a feature called “popularity change ratio” from existing data and then use a simplified but efficient algorithm based on “gain ratio” for decision tree construction. The final model is evaluated using common evaluation methods. This research is the first of its type in the educational field and represents a novel use of decision tree models with time series attributes for forecasting the popularity of Chinese colleges. Experimental analyses demonstrated encouraging results, proving the practical viability of the approach.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 675806, 7 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305704

Digital Object Identifier
doi:10.1155/2014/675806

Citation

Zeng, Xiangxiang; Yuan, Sisi; Li, You; Zou, Quan. Decision Tree Classification Model for Popularity Forecast of Chinese Colleges. J. Appl. Math. 2014 (2014), Article ID 675806, 7 pages. doi:10.1155/2014/675806. https://projecteuclid.org/euclid.jam/1425305704


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References

  • A. F. Mashat, M. Fouad, P. Yu, and T. Gharib, “A decision tree classification model for university admission system,” International Journal of Advanced Computer Science and Applications, vol. 3, no. 10, pp. 17–21, 2012.
  • J. Sun and H. Li, “Data mining method for listed companies' financial distress prediction,” Knowledge-Based Systems, vol. 21, no. 1, pp. 1–5, 2008.
  • H. Akaike, “Fitting autoregressive models for prediction,” Annals of the Institute of Statistical Mathematics, vol. 21, pp. 243–247, 1969.
  • G. E. P. Box and D. A. Pierce, “Distribution of residual autocorrelations in autoregressive-integrated moving average time series models,” Journal of the American Statistical Association, vol. 65, pp. 1509–1526, 1970.
  • R. Engle and V. Ng, “Measuring and testing the impact of news on volatility,” The Journal of Finance, vol. 48, no. 5, pp. 1749–1778, 1993.
  • Q. Zhang and A. Benveniste, “Wavelet networks,” IEEE Transactions on Neural Networks, vol. 3, no. 6, pp. 889–898, 1992.
  • L.-M. Wang, X.-L. Li, C.-H. Cao, and S.-M. Yuan, “Combining decision tree and Naive Bayes for classification,” Knowledge-Based Systems, vol. 19, no. 7, pp. 511–515, 2006.
  • M. J. Aitkenhead, “A co-evolving decision tree classification method,” Expert Systems with Applications, vol. 34, no. 1, pp. 18–25, 2008.
  • J. R. Quinlan and R. L. Rivest, “Inferring decision trees using the minimum description length principle,” Information and Computation, vol. 80, no. 3, pp. 227–248, 1989.
  • J. R. Quinlan, “Simplifying decision trees,” International Journal of Man-Machine Studies, vol. 27, no. 3, pp. 221–234, 1987.
  • J. R. Quinlan, C4. 5: Programs for Machine Learning, vol. 1, Morgan kaufmann, 1993.
  • L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, Wadsworth amd Brooks, Monterey, Calif, USA, 1984.
  • R. Duda and P. Hart, Pattern Classification and Scene Analysis, vol. 3, Wiley, New York, NY, USA, 1973.
  • J. Hanley, “Characteristic (ROC) curvel,” Radiology, vol. 743, pp. 29–36, 1982. \endinput