Integrating generative AI and machine learning classifiers for solving heterogenous MCGDM: a case of employee churn prediction
Bibliographic record
Abstract
Employee churn is a critical issue for companies and organizations, as it directly impacts productivity, efficiency, and overall operational success. High turnover rates increase recruitment and training costs, and disrupt workflows, making it a top concern for institutions aiming to maintain stability, growth and continuity. This study presents a methodology to address the employee churn prediction problem in heterogeneous environments by framing it as a Multiple Criteria Group Decision Making (MCGDM) problem. The proposed methodology integrates generative AI, Traditional MCGDM techniques, and machine learning classifiers to handle this problem type. The proposed methodology is structured into four main stages: data collection, generative AI for creating expert profiles, MCGDM for employee ranking, and machine learning for predictive modeling. ChatGPT-4 is used as the generative AI model to simulate expert profiles from diverse fields related to churn prediction. The Analytical Hierarchy Process (AHP) is employed to calculate criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is concerned with alternatives' ranking and employee's classification into churn likelihood categories. These rankings data are then used for different nine machine learning classifiers, reducing the computational complexity for future predictions. The results reveal that Neural Networks, Gradient Boosting, and Random Forest outperform other used models in predicting employee churn in terms of accuracy. The proposed methodology offers a scalable, data-driven solution for addressing MCGDM problems, particularly employee churn prediction, by integrating advanced AI techniques with traditional decision-making frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".