Applied and Computational Mathematics

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Prediction of Academic Talent Capacity Based on Gradient Boosting Decision Tree

Received: 05 August 2019    Accepted:     Published: 27 September 2019
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Abstract

Talent introduction is an important force of academic development in universities. As the core of talent introduction, prediction of academic talent capacity is an essential and valuable research. However, it is hard to apply traditional statistical methods to extract knowledge from the mass and multi-dimensional talent information. Data mining approaches as up-to-date and efficient technologies are good at analyzing information, extracting patterns or rules from a big dataset and then making a prediction based on the relationship among extracted information. In this study, a series of data mining approaches are employed to evaluate the academic capacity of talent and to analyze the correlation between features. The Principal Component Analysis and Random Forest are used to feature extraction for improving the accuracy of prediction. A classical classification model, Gradient Boosting Decision Tree, is used as the primary analytic model to prediction. In order to validate the effectiveness of the model, other five classification models are used to conduct a comparative experiment based on prediction accuracy values and the F-measure metric. Further, to investigate the contribution of some important features, we make a marginal utility analysis of important features which have a high correlation with academic talent capacity. The experiment results reveals the important features for academic capacity and the positive factors for the academic production of talents.

DOI 10.11648/j.acm.20190804.12
Published in Applied and Computational Mathematics (Volume 8, Issue 4, August 2019)
Page(s) 75-81
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Mining, Classification Models, Prediction, Talent Introduction, Academic Talent Capacity

References
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Author Information
  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

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  • APA Style

    Shunshun Shi, Mingzhou Chen, Rui Feng, Hua Zhang, Shuai Zhang. (2019). Prediction of Academic Talent Capacity Based on Gradient Boosting Decision Tree. Applied and Computational Mathematics, 8(4), 75-81. https://doi.org/10.11648/j.acm.20190804.12

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    ACS Style

    Shunshun Shi; Mingzhou Chen; Rui Feng; Hua Zhang; Shuai Zhang. Prediction of Academic Talent Capacity Based on Gradient Boosting Decision Tree. Appl. Comput. Math. 2019, 8(4), 75-81. doi: 10.11648/j.acm.20190804.12

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    AMA Style

    Shunshun Shi, Mingzhou Chen, Rui Feng, Hua Zhang, Shuai Zhang. Prediction of Academic Talent Capacity Based on Gradient Boosting Decision Tree. Appl Comput Math. 2019;8(4):75-81. doi: 10.11648/j.acm.20190804.12

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  • @article{10.11648/j.acm.20190804.12,
      author = {Shunshun Shi and Mingzhou Chen and Rui Feng and Hua Zhang and Shuai Zhang},
      title = {Prediction of Academic Talent Capacity Based on Gradient Boosting Decision Tree},
      journal = {Applied and Computational Mathematics},
      volume = {8},
      number = {4},
      pages = {75-81},
      doi = {10.11648/j.acm.20190804.12},
      url = {https://doi.org/10.11648/j.acm.20190804.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.acm.20190804.12},
      abstract = {Talent introduction is an important force of academic development in universities. As the core of talent introduction, prediction of academic talent capacity is an essential and valuable research. However, it is hard to apply traditional statistical methods to extract knowledge from the mass and multi-dimensional talent information. Data mining approaches as up-to-date and efficient technologies are good at analyzing information, extracting patterns or rules from a big dataset and then making a prediction based on the relationship among extracted information. In this study, a series of data mining approaches are employed to evaluate the academic capacity of talent and to analyze the correlation between features. The Principal Component Analysis and Random Forest are used to feature extraction for improving the accuracy of prediction. A classical classification model, Gradient Boosting Decision Tree, is used as the primary analytic model to prediction. In order to validate the effectiveness of the model, other five classification models are used to conduct a comparative experiment based on prediction accuracy values and the F-measure metric. Further, to investigate the contribution of some important features, we make a marginal utility analysis of important features which have a high correlation with academic talent capacity. The experiment results reveals the important features for academic capacity and the positive factors for the academic production of talents.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Prediction of Academic Talent Capacity Based on Gradient Boosting Decision Tree
    AU  - Shunshun Shi
    AU  - Mingzhou Chen
    AU  - Rui Feng
    AU  - Hua Zhang
    AU  - Shuai Zhang
    Y1  - 2019/09/27
    PY  - 2019
    N1  - https://doi.org/10.11648/j.acm.20190804.12
    DO  - 10.11648/j.acm.20190804.12
    T2  - Applied and Computational Mathematics
    JF  - Applied and Computational Mathematics
    JO  - Applied and Computational Mathematics
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    EP  - 81
    PB  - Science Publishing Group
    SN  - 2328-5613
    UR  - https://doi.org/10.11648/j.acm.20190804.12
    AB  - Talent introduction is an important force of academic development in universities. As the core of talent introduction, prediction of academic talent capacity is an essential and valuable research. However, it is hard to apply traditional statistical methods to extract knowledge from the mass and multi-dimensional talent information. Data mining approaches as up-to-date and efficient technologies are good at analyzing information, extracting patterns or rules from a big dataset and then making a prediction based on the relationship among extracted information. In this study, a series of data mining approaches are employed to evaluate the academic capacity of talent and to analyze the correlation between features. The Principal Component Analysis and Random Forest are used to feature extraction for improving the accuracy of prediction. A classical classification model, Gradient Boosting Decision Tree, is used as the primary analytic model to prediction. In order to validate the effectiveness of the model, other five classification models are used to conduct a comparative experiment based on prediction accuracy values and the F-measure metric. Further, to investigate the contribution of some important features, we make a marginal utility analysis of important features which have a high correlation with academic talent capacity. The experiment results reveals the important features for academic capacity and the positive factors for the academic production of talents.
    VL  - 8
    IS  - 4
    ER  - 

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