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access icon free Improvement on predicting employee behaviour through intelligent techniques

  • Author(s): Tarik A. Rashid 1  and  Asia Latif Jabar 2
    • View affiliations
    • Affiliations: 1: Software and Informatics Engineering, College of Engineering, Salahaddin University – Hawler, Kirkuk Street, Erbil 00964, Iraq ;
      2: Computer Department, College of Science, University of Sulaimani, Sulaimani, Kurdistan, Iraq
  • Source: Volume 5, Issue 5, September 2016, p. 136 – 142
    DOI:  10.1049/iet-net.2015.0106 , Print ISSN 2047-4954, Online ISSN 2047-4962
© The Institution of Engineering and Technology
Received 23/12/2015, Accepted 04/08/2016, Revised 01/07/2016, Published 01/09/2016

In recent times, there has been increasing awareness of employee behaviour prediction in healthcare, trade, and industry systems worldwide and its value on returns and profits of these systems. Nevertheless, determining the top employees with capacities and endorsing them for promotion is depending more or less on features which are dynamic and serving these systems’ interest. The current structure in organising and academic firms in Kurdistan-Iraq is non-systematic and manually performed; thus, the evaluation of employees’ behaviours is carried out by the directors at different branches, sections, and subsections; as a result, in some cases the outcomes of employees’ performance cause a low level of acceptance among staffs who believe that most of these cases are falsely assessed. This study suggests an intelligent and vigorous structure to examine performance of employees. It aims at presenting a solution to employee behaviour prediction through a joint effective feature selection method, then fuzzy rough (FR) set theory is used to select relevant features, next the classification task is conducted via FR nearest neighbours (FRNNs), decision tree, Naïve Bayes, and convolution neural network (CNN). FRNN and CNN classifiers have the best classification accuracy rate.

Inspec keywords: decision trees; feature selection; fuzzy set theory; Bayes methods; neural nets; pattern classification; personnel; behavioural sciences computing; rough set theory

Other keywords: fuzzy rough set theory; Naive Bayes; CNN classifiers; intelligent techniques; academic firms; FRNN; employee behaviour prediction; feature selection method; value on returns; decision tree; Kurdistan-Iraq; FRNN classifiers; convolution neural network; employee performance; FR nearest neighbours

Subjects: Neural computing techniques; Combinatorial mathematics; Other topics in statistics; Social and behavioural sciences computing; Data handling techniques

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