A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

This paper proposes a process for human resource performance evaluation using computational intelligence techniques. The human resource (or employee's) performance evaluation is essentially a regular assessment and review of an employee's performance on the job. This evaluation can be performed in different ways, depending on the kind of job of the employee and on the company's politics or business area. The process proposed on this research combines Fuzzy logic, text sentiment analysis and supervised learning classification techniques, such as a multi layer perceptron artificial neural network, decision tree algorithms and naïve bayes into ensemble classifiers, in an attempt to provide a fair evaluation process, minimizing or even eliminating common problems caused by simple objective or subjective approaches. The data provided for this research was originated from several evaluations applied in two Brazilians institutions. Simulation results shows consistence on the data generated by this proposed process, indicating a good perspective for applications on companies of most business areas.