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

Abstract

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.

Examining child obesity risk level using fuzzy inference system

“…Variable defining stages Sugeno fuzzy inference system will provide results or output after doing the variable definition stage. Research in determining the level of risk of obesity in children, researchers used six input variables, namely: -Input variable with trapezoid curve [32], [33] also uses trapezoidal curves in his research.…”

Detecting Dehydration Based on Urine Color Using Fuzzy Logic Image Processing and Regulating Water Intake with an Automatic Water Pump According to Dehydration Level Using an IoT-Based

“…[22] The membership function of each input variable can be seen in the picture In the table 2 above, explains the membership function for BLUE After determining the membership function for each input variable [12] and output variabel, the next step is to create a rule. At this stage, each output from the fuzzification stage in the form of membership degrees and linguistic variables will be combined using an evaluation rule [23]. In rule evaluation, there are linguistic rules to determine the control action on the input value from defuzzification.…”

Considerations and important aspects for project management

“…The creation of standards to evaluate an employee's performance with the purpose of identifying problems such as supervision, integration into the position or project, lack of expected performance, motivation, among others (Osmani & Maliqi, 2012;Góes & Oliveira, 2020;Wang, 2022). This process helps create appropriate policies for the project.…”