Improvement on predicting employee behaviour through intelligent techniques. Issue 5 (1st September 2016)

• Record Type: Journal Article
• Authors: Rashid, Tarik A.; Jabar, Asia Latif
• Is Part Of: IET networks. Volume 5:Issue 5(2016)
• Page Start: 136
• Page End: 142
• Publication Date: 2016-09-01
• Subjects: personnel -- pattern classification -- behavioural sciences computing -- fuzzy set theory -- rough set theory -- feature selection -- decision trees -- Bayes methods -- neural nets; employee behaviour prediction -- intelligent techniques -- value on returns -- academic firms -- Kurdistan‐Iraq -- employee performance -- feature selection method -- fuzzy rough set theory -- FRNN -- FR nearest neighbours -- decision tree -- Naive Bayes -- convolution neural network -- CNN classifiers -- FRNN classifiers; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2015.0106
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : 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.