Precise stipend of undergraduates based on multi-view and classification ensemble
Authors: 
Fangjuan Zhang, Yan YangAuthors Info & Claims
Fangjuan Zhang, Yan YangAuthors Info & ClaimsPages 117 - 118
Abstract
With the development of education informatization, the data-driven education reform has gradually become a research hotspot. The arrival of digital campus brings new opportunities for the higher education institutions to promote convenient and efficient precise stipend. In this paper, a model based on multi-view and classification ensemble is proposed to predict the stipend of undergraduates. Firstly, the multi-dimensional data of undergraduates is divided into two different views according to their learning performance and living behavior. Then, the multi-view learning methods are used to obtain more discriminative features, and the classification ensemble is applied to predict the stipend of undergraduates at last. The experimental results show that the proposed model based on multi-view and classification ensemble can effectively achieve the stipend prediction.
References
[1]
A. Mishra, R. Bansal, and S. Singh. Educational data mining and learning analysis. In Proceedings of the International Conference on Cloud Computing, Data Science & Engineering-Confluence, pages 491--494. IEEE, 2017.
[2]
O. Kochetkov and I. Prokhorov. The research of approaches of applying the results of big data analysis in higher education. In Proceedings of the American Institute of Physics Conference Series, pages 1797: 020008-1--020008-7. AIP, 2017.
[3]
T. Sun, S. Chen, and J. Yang. A novel method of combined feature extraction for recognition. In Proceedings of the Eighth IEEE International Conference on Data Mining, pages 1043--1048. IEEE, 2008.
[4]
X. Zhou, X. Chen, and S. Chen. Combined-feature-discriminability enhanced canonical correlation analysis. Pattern Recognition and Artificial Intelligence, 25(2):285--291, 2012.
[5]
J. Zhang, Y. Wu, and J. Bai. Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers. Transactions of the Institute of Measurement and Control, 38(4):435--451, 2016.
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May 2018
139 pages
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 May 2018
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- National Science Foundation of China
- The Educational Reform Project of Southwest Jiaotong University
- Educational Reform Project of Southwest Jiaotong University
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TURC 2018
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