Affiliations: [a] Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, U.P., India
| [b] Department of Computer Science & Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India
Correspondence:
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Corresponding author. Rohit Mishra, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, U.P., India. E-mail: rohit129.iiita@gmail.com.
Abstract: Impreciseness and uncertainty are the fabrics that make life interesting. For decades, human beings have developed strategies to cope with uncertainties and automate them. In personnel selection for the I.T. field, selectors often find it very difficult to select candidates by going through a set of resumes containing similar kinds of skills. Hence the selection task becomes a fuzzy decision making with the uncertainty involved. A combination of fuzzy clustering and Interval Type-2 fuzzy sets (IT2FS) is proposed in such scenarios. An experiment is conducted over a resume dataset containing fifteen hundred resumes for a particular job description. Firstly, Fuzzy C-means clustering (FCM) is applied for selective clustering, while decision-making under uncertainty is carried through IT2FS. The candidates in the selected cluster are given a score for ranking as per the skillset criteria. The final decision for shortlisting the resumes is carried through IT2FS. The model shows an average accuracy of 88.2% with an F1-score of 0.76 compared to (K-means + IT2FS) model with an F1-score of 0.72. Thus, the proposed model performs better while decision-making under uncertainty.
