Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining

Xiao Yang; Caiyun Ying; Yefeng Zhou

doi:doi:10.11648/j.ijsd.20180401.13

| Peer-Reviewed

Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining

Xiao Yang, Caiyun Ying, Yefeng Zhou

Published in International Journal of Statistical Distributions and Applications (Volume 4, Issue 1)

Received: 26 April 2018 Published: 27 April 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.

Published in	International Journal of Statistical Distributions and Applications (Volume 4, Issue 1)
DOI	10.11648/j.ijsd.20180401.13
Page(s)	22-28
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), 2018. Published by Science Publishing Group

Keywords

Talent Introduction Strategies, Apriori Algorithm, Prediction Model, R Language

References

[1]	R. Baker, E. Duval, J. Stamper, D. Wiley, and S. B. Shum, “Educational data mining meets learning analytics,” Technol. Knowl. Learn., 2014, vol. 19, pp. 205-220.
[2]	X. Gong, and S. X. Lin, “Construction of evaluation system of sports talent training scheme based on data mining,” J. Residuals Sci. Technol., 2016, vol. 13, pp. 343-349.
[3]	K. Lv, and Q. Wang, “Data mining in traditional sports talent training program decision making,” Advan. Soci. Sci. Educ. Human. Resea., 2016, vol. 71, pp. 767-770.
[4]	F. Li, S. Ge, and J. Yin, “Research on talent introduction strategies hazard and training strategy of university based on data mining,” Comp. Risk Mgmt., 2011, vol. 5, pp. 219-224.
[5]	D. P. Zhang, and D. Jin, “The data mining of the human resources data warehouse in university based on association rule,” J. Comp., 2011, vol. 6, pp. 139-146.
[6]	J. Ranjan, D. P. Goyal, and S. I. Ahson, “Data mining techniques for better decisions in human resource management systems,” Int. J. of Business Information Systems, 2008, vol. 3, pp. 464-481.
[7]	G. Wei, F. E. Alsaadi, T. Hayat, “A linear assignment method for multiple criteria decision analysis with hesitant fuzzy sets based on fuzzy measure,” Int. J. Fuzzy Syst., 2017, vol. 19, pp. 607-614.
[8]	R. Agrawal, T. Imielinski, and A. N. Swami, “Mining association rules between sets of items in large databases,” Sigmod Rec., 1993, vol. 22, pp. 207-216.
[9]	F. B. Kamsu, F. Rigal, and F. Mauget, “Mining association rules for the quality improvement of the production process,” Expert Syst. Appl., 2013, vol. 40, pp. 1034-1045.
[10]	Y. Liu, C. Wang, and N. N. Wang, “Application of apriori association rule mining algorithm in university management system,” J. Residuals Sci. Technol., 2016, vol. 13, pp. 601-604.
[11]	J. Friedman, T. Hastie, and R. Tibshirani, “Additive logistic regression: a statistical view of boosting,” Ann. Stat., 2000, vol. 28, pp. 337-407.
[12]	J. R. Quinlan, “Induction of decision trees,” Mach. Learn., 1986, vol. 1, pp. 81-106.
[13]	Z. Q. Qi, T. Ying, and Y. Shi, “Robust twin support vector machine for pattern classification,” Pattern Recogn., 2013, vol. 46, pp. 305-316.
[14]	J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, and P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med., 2001, vol. 7, pp. 673-679.

Cite This Article

Plain Text BibTeX RIS

APA Style

Xiao Yang, Caiyun Ying, Yefeng Zhou. (2018). Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. International Journal of Statistical Distributions and Applications, 4(1), 22-28. https://doi.org/10.11648/j.ijsd.20180401.13

Copy | Download

ACS Style

Xiao Yang; Caiyun Ying; Yefeng Zhou. Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. Int. J. Stat. Distrib. Appl. 2018, 4(1), 22-28. doi: 10.11648/j.ijsd.20180401.13

Copy | Download

AMA Style

Xiao Yang, Caiyun Ying, Yefeng Zhou. Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. Int J Stat Distrib Appl. 2018;4(1):22-28. doi: 10.11648/j.ijsd.20180401.13

Copy | Download

@article{10.11648/j.ijsd.20180401.13,
  author = {Xiao Yang and Caiyun Ying and Yefeng Zhou},
  title = {Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {4},
  number = {1},
  pages = {22-28},
  doi = {10.11648/j.ijsd.20180401.13},
  url = {https://doi.org/10.11648/j.ijsd.20180401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20180401.13},
  abstract = {Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining
AU  - Xiao Yang
AU  - Caiyun Ying
AU  - Yefeng Zhou
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijsd.20180401.13
DO  - 10.11648/j.ijsd.20180401.13
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 22
EP  - 28
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20180401.13
AB  - Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.
VL  - 4
IS  - 1
ER  -

Copy | Download

Author Information

Xiao Yang

School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
Caiyun Ying

School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
Yefeng Zhou

School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Xiao Yang, Caiyun Ying, Yefeng Zhou. (2018). Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. International Journal of Statistical Distributions and Applications, 4(1), 22-28. https://doi.org/10.11648/j.ijsd.20180401.13

Copy | Download

ACS Style

Xiao Yang; Caiyun Ying; Yefeng Zhou. Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. Int. J. Stat. Distrib. Appl. 2018, 4(1), 22-28. doi: 10.11648/j.ijsd.20180401.13

Copy | Download

AMA Style

Xiao Yang, Caiyun Ying, Yefeng Zhou. Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. Int J Stat Distrib Appl. 2018;4(1):22-28. doi: 10.11648/j.ijsd.20180401.13

Copy | Download

@article{10.11648/j.ijsd.20180401.13,
  author = {Xiao Yang and Caiyun Ying and Yefeng Zhou},
  title = {Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {4},
  number = {1},
  pages = {22-28},
  doi = {10.11648/j.ijsd.20180401.13},
  url = {https://doi.org/10.11648/j.ijsd.20180401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20180401.13},
  abstract = {Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining
AU  - Xiao Yang
AU  - Caiyun Ying
AU  - Yefeng Zhou
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijsd.20180401.13
DO  - 10.11648/j.ijsd.20180401.13
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 22
EP  - 28
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20180401.13
AB  - Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.
VL  - 4
IS  - 1
ER  -

Copy | Download