Abstract
In a changing environment and inaccurate information, it is difficult to get an unambiguous answer about the quality of the candidate for the position, based only on the results of viewing the applicant’s questionnaires. As a consequence, recently there has been a trend towards the use of soft computing (neural networks, fuzzy logic and evolutionary computations) in tasks personnel’s selection. The article presents the solution of such a problem using the methods of soft computing for a software company. We use a neural-fuzzy system such as the ANFIS (Adaptive Network-Based Fuzzy Inference System) to quantify the candidate’s quality. The idea of neural-fuzzy systems is to determine the parameters of fuzzy systems through training methods used in neural networks. The most important advantage of this system lies in the automatic creation of the rules base. After completing the training, we receive an assessment of the quality of the candidate in the form of a scoring on a 10-point scale. In addition, we derive a regression equation that relates the candidate’s quality with the input variables.
Published Version
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