Improving Salary Offer Processes With Classification Based Machine Learning Models - Kaya, Rukiye; Saatci, Mehtap; Bakal, Gokhan; Bakal, Mehmet Gokhan (Institute of Electrical and Electronics Engineers Inc., 2024)
In job applications, salary is major motivational factor for employees and making accurate salary prediction is crucial for both employers and employees. Utilizing advanced technologies can significantly enhance the accuracy and efficiency of salary prediction process. In this study, we explore Machine Learning (ML) methods to enhance salary prediction process. We evaluated seven classification models for predicting salary categories, with the Artificial Neural Network (ANN) model achieving the highest accuracy at 58.2% on the test dataset, followed by the K-Nearest Neighbors (KNN) model with an accuracy of 56.8%. Additionally, we employed ensemble models to further enhance prediction accuracy. Among these, the Majority Voting Classifier using Hard Voting achieved the highest accuracy at 59.3%, demonstrating the potential of ensemble techniques in refining salary predictions. The developed salary prediction tool estimates the most appropriate salary category for each candidate and help mitigate potential biases in manual salary assessments, hence enables a more objective and consistent compensation system. ∗CRITICAL: Do Not Use Symbols, Special Characters, or Math in Paper Title or Abstract, and do not cite other papers in the abstract. © 2024 Elsevier B.V., All rights reserved.
Keywords: Artificial Neural Network (Ann); K-Nearest Neighbors (Knn); Machine Learning; Salary Prediction; Adversarial Machine Learning; Contrastive Learning; Earnings; K-Nearest Neighbors; Neural Network Models; Artificial Neural Network; High-Accuracy; Job Application; K-Near Neighbor; Machine Learning Models; Machine-Learning; Nearest-Neighbour; Neural-Networks; Prediction Process; Salary Prediction; Prediction Models