• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 4, Pages: 325-334

Original Article

An adaptive intelligent framework for assessment & selection process in staffing task

Received Date:29 November 2020, Accepted Date:21 January 2021, Published Date:02 February 2021


Objectives: To enhance the performance of HR’s staffing function by providing an intelligent framework that allows convenient assessment and selection procedures. Methods: We proposed a new approach that mainly uses Data Mining (DM) and Machine Learning (ML) to develop and train an intelligent framework by learning the behavior of the staffing committee in assessing and selecting applicants for specific job requirements. It utilizes fuzzy logic to mitigate the decision uncertainty and provide an objective mechanism for filtering best-fit applicants’ profiles for the next selection phase. The proposed framework was trained on a labeled dataset consisted of (414) CVs. A 5- fold cross-validation method was used to train and evaluate the proposed framework. The highest accuracy achieved was (84%) at k=2); while the lowest accuracy achieved was (71%) at K=1. Findings: The accuracy performance is at acceptable levels and can be improved as more data involved in the training process.

Keywords: Staffing; data mining; fuzzy logic; machine learning


  1. Alhabashneh OYA. Coventry University An Adaptive Fuzzy Based Recommender System For Enterprise Search. 2015.
  2. Feldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge university press. 2007.
  3. Bazylak J, Weiss PE. Online Evolution: Advantages and Challenges of Online Course Components. Proceedings of the Canadian Engineering Education Association (CEEA). 2018. Available from: https://dx.doi.org/10.24908/pceea.v0i0.10182
  4. Ghosh S, Roy S, Bandyopadhyay SK. A tutorial review on Text Mining Algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 2012;1(4):7.
  5. Jayaraj V, Mahalakshmi V. Augmenting Efficiency of Recruitment Process using IRCF text mining Algorithm. Indian Journal of Science and Technology. 2015;8(16). Available from: https://dx.doi.org/10.17485/ijst/2015/v8i16/53381
  6. Chuang Z, Ming W, Guang LC, Bo X, Zhi-Qing L. Resume parser: Semi-structured chinese document analysis. In: 2009 WRI World Congress on Computer Science and Information Engineering. (Vol. 5, pp. 12-16) 2009.
  7. Kopparapu SK. Automatic extraction of usable information from unstructured resumes to aid search. 2010 IEEE International Conference on Progress in Informatics and Computing. 2010;1:99–103.
  8. Jiang Z, Zhang C, Xiao B, Lin Z. Research and implementation of intelligent chinese resume parsing. 2009 WRI International Conference on Communications and Mobile Computing. 2009;3:588–593.
  9. Anand MCJ, Bharatraj J. Theory of triangular fuzzy number. Proc. NCATM. 2017;p. 80.
  10. Renkas K, Niewiadomski A. Learning rules for hierarchical fuzzy logic systems using Wu & mendel IF-THEN rules quality measures. International Conference on Artificial Intelligence and Soft Computing. 2016;p. 299–310.
  11. Doctor F, Iqbal R. An intelligent framework for monitoring student performance using fuzzy rule-based linguistic summarisation. 2012 IEEE International Conference on Fuzzy Systems. 2012;p. 1–8.
  12. Ishibuchi H, Yamamoto T. Rule weight specification in fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems. 2005;13(4):428–435. Available from: https://dx.doi.org/10.1109/tfuzz.2004.841738
  13. Steiger NM, Steiger DM. Knowledge Mining in DSS Model Analysis. Organ. Data Min. Leveraging Enterp. Data Resour. Optim. Perform. 2004;p. 157–169.
  14. Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statistics Surveys. 2010;4(0):40–79. Available from: https://dx.doi.org/10.1214/09-ss054


© 2021 Tabaza et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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