Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning

Yuhang Fan; Pengjing Xu; Yue Zhu; Miao Chen

doi:10.1109/CIPAE55637.2022.00016

Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning

Yuhang Fan
, Pengjing Xu^*
, Yue Zhu
, Miao Chen

^*Corresponding author for this work

Shanghai Science and Technology Innovation Resources Center

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

Talent evaluation is an important part for our country to discover outstanding talents, to allocate human resources according to market, to motivate innovation and entrepreneurship. At present, domestic talent evaluation is mostly carried out in the traditional organizational expert mode, and there is huge space for improvement in efficiency and cost. Based on multiple batches of structured candidate talent index data, this study uses an integrated learning model to simulate and predict talent scores, which can assist experts on their work and improve assessment efficiency. Results from our experiments show, even in the case of small data sets, the scoring process based on our model can have the average error about 6 percentage between the predicted score and the actual score, and the error can be further reduced when the amount of data increase.

Original language	English
Title of host publication	Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	30-34
Number of pages	5
ISBN (Electronic)	9781665468121
DOIs	https://doi.org/10.1109/CIPAE55637.2022.00016
Publication status	Published - 2022
Externally published	Yes
Event	3rd International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022 - Ottawa, Canada Duration: 26 Aug 2022 → 28 Aug 2022

Publication series

Name	Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022

Conference

Conference	3rd International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022
Country/Territory	Canada
City	Ottawa
Period	26/08/22 → 28/08/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ensemble learning
expert decision-making
machine learning
talent evaluation

Access to Document

10.1109/CIPAE55637.2022.00016

Cite this

Fan, Y., Xu, P., Zhu, Y., & Chen, M. (2022). Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning. In Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022 (pp. 30-34). (Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIPAE55637.2022.00016

Fan, Yuhang ; Xu, Pengjing ; Zhu, Yue et al. / Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning. Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 30-34 (Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022).

@inproceedings{e2b14623764845fcb8764a51a10cf074,

title = "Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning",

abstract = "Talent evaluation is an important part for our country to discover outstanding talents, to allocate human resources according to market, to motivate innovation and entrepreneurship. At present, domestic talent evaluation is mostly carried out in the traditional organizational expert mode, and there is huge space for improvement in efficiency and cost. Based on multiple batches of structured candidate talent index data, this study uses an integrated learning model to simulate and predict talent scores, which can assist experts on their work and improve assessment efficiency. Results from our experiments show, even in the case of small data sets, the scoring process based on our model can have the average error about 6 percentage between the predicted score and the actual score, and the error can be further reduced when the amount of data increase.",

keywords = "ensemble learning, expert decision-making, machine learning, talent evaluation",

author = "Yuhang Fan and Pengjing Xu and Yue Zhu and Miao Chen",

note = "Funding Information: ACKNOWLEDGMENT This study was conducted at Data Science and Artificial Intelligence Institute of Shanghai Science and Technology Innovation Resources Center, supported by STCSM Grands No:21PJ1421300 and No:18510745600.. Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022 ; Conference date: 26-08-2022 Through 28-08-2022",

year = "2022",

doi = "10.1109/CIPAE55637.2022.00016",

language = "English",

series = "Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "30--34",

booktitle = "Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022",

}

Fan, Y, Xu, P, Zhu, Y & Chen, M 2022, Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning. in Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022. Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022, Institute of Electrical and Electronics Engineers Inc., pp. 30-34, 3rd International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022, Ottawa, Canada, 26/08/22. https://doi.org/10.1109/CIPAE55637.2022.00016

Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning. / Fan, Yuhang; Xu, Pengjing; Zhu, Yue et al.
Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 30-34 (Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning

AU - Fan, Yuhang

AU - Xu, Pengjing

AU - Zhu, Yue

AU - Chen, Miao

N1 - Funding Information: ACKNOWLEDGMENT This study was conducted at Data Science and Artificial Intelligence Institute of Shanghai Science and Technology Innovation Resources Center, supported by STCSM Grands No:21PJ1421300 and No:18510745600.. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Talent evaluation is an important part for our country to discover outstanding talents, to allocate human resources according to market, to motivate innovation and entrepreneurship. At present, domestic talent evaluation is mostly carried out in the traditional organizational expert mode, and there is huge space for improvement in efficiency and cost. Based on multiple batches of structured candidate talent index data, this study uses an integrated learning model to simulate and predict talent scores, which can assist experts on their work and improve assessment efficiency. Results from our experiments show, even in the case of small data sets, the scoring process based on our model can have the average error about 6 percentage between the predicted score and the actual score, and the error can be further reduced when the amount of data increase.

AB - Talent evaluation is an important part for our country to discover outstanding talents, to allocate human resources according to market, to motivate innovation and entrepreneurship. At present, domestic talent evaluation is mostly carried out in the traditional organizational expert mode, and there is huge space for improvement in efficiency and cost. Based on multiple batches of structured candidate talent index data, this study uses an integrated learning model to simulate and predict talent scores, which can assist experts on their work and improve assessment efficiency. Results from our experiments show, even in the case of small data sets, the scoring process based on our model can have the average error about 6 percentage between the predicted score and the actual score, and the error can be further reduced when the amount of data increase.

KW - ensemble learning

KW - expert decision-making

KW - machine learning

KW - talent evaluation

UR - https://www.scopus.com/pages/publications/85148623145

U2 - 10.1109/CIPAE55637.2022.00016

DO - 10.1109/CIPAE55637.2022.00016

M3 - Conference Proceeding

AN - SCOPUS:85148623145

T3 - Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022

SP - 30

EP - 34

BT - Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022

Y2 - 26 August 2022 through 28 August 2022

ER -

Fan Y, Xu P, Zhu Y, Chen M. Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning. In Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 30-34. (Proceedings - 2022 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022). doi: 10.1109/CIPAE55637.2022.00016

Research on the Evaluation Method of Auxiliary Expert Talents Based on Ensemble Learning

Abstract

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UN SDGs

Keywords

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