RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers

• DOI: 10.3390/math10193714
• Publication Date: 2022-10-11T01:07:11Z
• Authors: Keyur Patel; Karan Sheth; Dev Mehta; Sudeep Tanwar; Bogdan Cristian F...; Dragos Daniel Tar...; Ahmed Altameem; Torki Altameem; Ravi Sharma
• Keywords: Rank (graph theory)

An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee enhances productivity output of organizations, i.e., employee paves way for success. Hence, analyzing giving ratings to employees essential companies nowadays. It evident that different people have skill sets behavior, so data should be gathered from all parts life. This paper aims provide rating based various factors. First, we compare AI-based algorithms, such as random forest, artificial neural network, decision tree, XGBoost. Then, propose ensemble approach, RanKer, combining above approaches. The empirical results illustrate efficacy proposed model compared traditional models XGBoost high in terms precision, recall, F1-score, accuracy.