Skip to main content

Advertisement

Springer Nature Link
Log in

Evaluation and analysis of human resource management mode and its talent screening factors based on decision tree algorithm

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Human resource management is the cornerstone of enterprise success. In the process of enterprise management and control, the design of human resource management mode is a very important part of its management. Data mining technology provides a valuable and meaningful knowledge of extracting and mining big data. And such a technology will be an advantageous tool for human resource experts in the face of difficult and unknown talent screening. In the past, talent screening relied on many factors, such as experience, knowledge, performance and judgment ability. The screening criteria are no longer sufficient, because in this knowledge economy and business environment, the factors that facilitate someone to sit in a certain position today may not apply the next day, but in talent management, the definition of output is ensured that the right people are in the right jobs. Based on the above factors, how to select talents in the field of talent management and predict the possible future development of talents has become a challenge and problem for every organization. In this research, this research proposed data mining method with the decision tree technology to analyze data and find out the key factors that affect the on-the-job time through mining data. The results show that extended the application of data mining to the field of human resource management. Through the decision tree technology, this research contributions are companies can improve their recruitment methods and pay more attention to job seekers in the location. The policy of employee retention and employee recruitment will be improved. The key factors that affect the in-service time are predicted, and some key information that may affect the in-service time is obtained. This will help the company make correct decisions and effectively reduce the cost of company operations. This will help the company make correct decisions and effectively reduce the cost of company operations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Li M, Wang H, Li J (2020) Mining conditional functional dependency rules on big data. Big Data Min Anal 3(1):68–84

    Article  Google Scholar 

  2. Yin Y, Long L, Deng X (2020) Dynamic data mining of sensor data. IEEE Access 8:41637–41648

    Article  Google Scholar 

  3. Danlu Liu E, Baskett W, Beversdorf D, Shyu C-R (2020) xploratory data mining for subgroup cohort discoveries and prioritization. IEEE J Biomed Health Inform 24(5):1456–1468

    Article  Google Scholar 

  4. Ye J, Yang J, Yu J, Tan S, Luo F, Yuan Z, Chen Y (2021) A Chi-MIC based adaptive multi-branch decision tree. IEEE Access 9:78962–78972

    Article  Google Scholar 

  5. Lopez D, Sigrist L (2017) A centralized UFLS scheme using decision trees for small isolated power systems. IEEE Latin Am Trans. 15(10):1888–1893

    Article  Google Scholar 

  6. Barros RC, Basgalupp MP, Freitas AA, de Carvalho ACPLF (2014) Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets. IEEE Trans Evolut Comput. 18(6):873–892

    Article  Google Scholar 

  7. Chi C-F, Tseng L-K, Jang Y (2012) Pruning a decision tree for selecting computer-related assistive devices for people with disabilities. IEEE Trans Neural Syst Rehabil Eng 20(4):564–573

    Article  Google Scholar 

  8. Li Y, Dong M, Kothari R (2005) Classifiability-based omnivariate decision trees. IEEE Trans Neural Netw. 16(6):1547–1560

    Article  Google Scholar 

  9. Lim H, Choe Y, Shim M, Lee J (2014) A quad-trie conditionally merged with a decision tree for packet classification. IEEE Commun Lett 18(4):676–679

    Article  Google Scholar 

  10. Rivera-Lopez R, Canul-Reich J (2018) Construction of near-optimal axis-parallel decision trees using a differential-evolution-based approach. IEEE Access. 6:5548–5563

    Article  Google Scholar 

  11. Chen CM, Chen L, Gan W, Qiu L, Ding W (2021) Discovering high utility-occupancy patterns from uncertain data. Inf Sci 546:1208–1229

    Article  MathSciNet  Google Scholar 

  12. Chen CM, Huang Y, Wang KH, Kumari S, Wu M (2021) A secure authenticated and key exchange scheme for fog computing. Enterprise Inf Syst 15(9):1200–11215

    Article  Google Scholar 

  13. Chen X, Li A, Zeng X, Guo W (2015) Huang G (2015) Runtime model based approach to IoT application development. Front Comp Sci 9(4):540–553

    Article  Google Scholar 

  14. Chen X, Lin J, Ma Y, Lin B, Wang H, Huang G (2019) Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model. Sci China Inform Sci 62(11):219101

    Article  Google Scholar 

  15. Chen X, Wang H, Ma Y, Zheng X, Guo L (2020) Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model. Futur Gener Comput Syst 105:287–296

    Article  Google Scholar 

  16. Choi B, Ravichandran T, O'Connor GC (2019) Organizational conservatism, strategic human resource management, and breakthrough innovation. IEEE Trans Eng Manag 66(4):529–541

    Article  Google Scholar 

  17. Chiang HY, Lin BMT (2020) A decision model for human resource allocation in project management of software development. IEEE Access 8:38073–38081

    Article  Google Scholar 

  18. Galli B (2017) HRM's importance throughout the organization: analyzed through the concepts and views portrayed in eli goldratt’s the goal. IEEE Eng Manag Rev 45(3):54–63

    Article  Google Scholar 

  19. Kim T-H, Kumar G, Saha R, Rai MK, Buchanan WJ, Thomas R, Alazab M (2020) A privacy preserving distributed ledger framework for global human resource record management: the blockchain aspect. IEEE Access 8:96455–96467

    Article  Google Scholar 

  20. Ali MM, Rajamani L (2012) Automation of Decision Making Process for Selection of Talented Manpower Considering Risk Factor: A Data Mining Approach. 2012 International Conference on Information Retrieval & Knowledge Management

  21. Huang G, Liu X, Ma Y, Lu X, Zhang Y, Xiong Y (2019) Programming situational mobile web applications with cloud-mobile convergence: an internetware-oriented approach. IEEE Trans Serv Comput 12(1):6–19

    Article  Google Scholar 

  22. Huang G, Ma Y, Liu X, Luo Y, Lu X, Blake M (2015) Model-based automated navigation and composition of complex service mashups. IEEE Trans Serv Comput 8(3):494–506

    Article  Google Scholar 

  23. Huang G, Xu M, Lin X, Liu Y, Ma Y, Pushp S, Liu X (2017) ShuffleDog: characterizing and adapting user-perceived latency of android apps. IEEE Trans Mob Comput 16(10):2913–2926

    Article  Google Scholar 

  24. Lin B, Huang Y, Zhang J, Hu J, Chen X, Li J (2020) Cost-driven offloading for DNN-based applications over cloud, edge and end devices. IEEE Trans Industr Inf 16(8):5456–5466

    Article  Google Scholar 

  25. Liu X, Huang G, Zhao Q, Mei H, Blake M (2014) iMashup: a mashup-based framework for service composition. Sci China Inf Sci 54(1):1–20

    Article  Google Scholar 

  26. Wang J, Sun Y, Zhang Z, Gao S (2020) Solving multitrip pickup and delivery problem with time windows and manpower planning using multiobjective algorithms. IEEE/CAA J Autom Sinica 7(4):1134–1153

    Article  Google Scholar 

  27. Ye O, Huang P, Zhang Z, Zheng Y et al (2021) Multiview learning with robust double-sided twin SVM, IEEE Transactions on Cybernetics. (early Access)

  28. Fu L, Li Z, Ye Q, et al (2020) Learning robust discriminant subspace based on joint L2,p- and L2,s-norm distance metrics, IEEE Transactions on Neural Networks and Learning Systems. pp 93–100 (Early Access)

  29. Ye Q, Li Z, Fu L et al (2019) Nonpeaked discriminant analysis. IEEE Trans Neural Netw Learn Syst 30(12):3818–3832

    Article  MathSciNet  Google Scholar 

  30. Liu T, Zhang L (2021) Apply artificial neural network to solving manpower scheduling problem. In: 2021 IEEE 4th international conference on big data and artificial intelligence (BDAI)

  31. Halstead JB (2009) Recruiter selection model and implementation within the United States Army. IEEE Trans Syst Man Cybern Part C 39(1)

Download references

Author information

Authors and Affiliations

  1. Logistics Department, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China

    Chuanzhu Zhang

Authors
  1. Chuanzhu Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chuanzhu Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C. Evaluation and analysis of human resource management mode and its talent screening factors based on decision tree algorithm. J Supercomput 78, 15681–15713 (2022). https://doi.org/10.1007/s11227-022-04499-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04499-z

Keywords