Employee promotion prediction by using machine learning algorithms for imbalanced dataset
| dc.authorid | 0000-0002-8076-3678 | |
| dc.contributor.author | Şahinbaş, Kevser | |
| dc.date.accessioned | 2022-10-19T11:35:17Z | |
| dc.date.available | 2022-10-19T11:35:17Z | |
| dc.date.issued | 2022 | |
| dc.department | İstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü | |
| dc.description.abstract | Promotion processes are one of the most important processes in terms of human resources. A promotion process organized fairly within the organization is a managerial tool that motivates employees and contributes to business continuity. Promotion is an important extrinsic motivation for many employees. It ensures the employee's engagement and commitment to the organization and contributes to the continuity of his current performance. It is also an important rewarding and performance control mechanism for the organization. Many factors such as seniority, performance level, competencies, age, awards, training score, organizational commitment of the personnel who will be promoted are taken into consideration. In this study, a prediction methodology will be studied based on the criteria evaluated for the employees in the promotion processes by Machine Learning algorithms such as Support Vector Machine, Artificial Neural Network, and Random Forest. Random Forest achieved the highest performance with 98% accuracy, 96% precision, 1.0% recall and 98% f1-score values with ROS approach. This study could be used by HR and manager to predict the probability of promotion so that managers can find the right parameters for someone to get promoted. | |
| dc.description.sponsorship | IEEE Turkey Section ; Istanbul Atlas University | en_US |
| dc.identifier.citation | Şahinbaş, K. (2022). Employee promotion prediction by using machine learning algorithms for imbalanced dataset. 2nd International Conference on Computing and Machine Intelligence, ICMI. Istanbul, 15-16 July 2022. https://doi.org/10.1109/ICMI55296.2022.9873744 | |
| dc.identifier.doi | 10.1109/ICMI55296.2022.9873744 | |
| dc.identifier.isbn | 9781665474832 | |
| dc.identifier.scopus | 2-s2.0-85139027279 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://doi.org/10.1109/ICMI55296.2022.9873744 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/9847 | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Şahinbaş, Kevser | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2nd International Conference on Computing and Machine Intelligence, ICMI | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | ANN | |
| dc.subject | Data Management | |
| dc.subject | Employee Promotion | |
| dc.subject | HR Dataset | |
| dc.subject | Imbalanced Dataset | |
| dc.subject | Prediction | |
| dc.subject | RF | |
| dc.subject | SVM | |
| dc.title | Employee promotion prediction by using machine learning algorithms for imbalanced dataset | |
| dc.type | Conference Object |
