Employee Attrition Prediction using an Explainable FT-Transformer Deep Learning Model

DOI

10.2991/978-94-6239-654-8_50How to use a DOI?

Keywords

Employee Attrition Prediction; Deep Learning; FT-Transformer; Explainable Artificial Intelligence (XAI); SHAP; IBM HR Analytics Employee Attrition; HR Professional; Decision-Support systems

Abstract

As the attrition rate increases, it is difficult for Human Resource professionals to predict the influencing factors. Taking necessary actions at the right time to retain employee will reduces employee recruitment costs become difficult for Human Resource Personnel Therefore there is a need for efficient decision making system for Human Resource Management. In this study, to predict Employee Attrition, one of the advanced Deep learning techniques called Feature Tokenizer Transformer (FT-Transformer) architecture model was implemented which works efficiently for tabular type of datasets. The dataset used in this study was the IBM HR Analytics Employee Attrition obtained from Kaggle Repository. Performance of the model was evaluated using standard metrics of accuracy, precision, recall, F1-score, and Area Under the ROC curve (AUC) and along with confusion matrix. An Explainable Artificial Intelligence (XAI) method called SHAP (SHapley Additive exPlanations) was applied to the FT-Transformer predictions to know the strong attributes that influences an employee to leave an organization. The Proposed model achieved an accuracy of 87.07% and an AUC of 0.763 with highest SHAP value 0.041003 for the attribute age.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Kanthimathi
AU  - T. S. Aarathy
PY  - 2026
DA  - 2026/04/24
TI  - Employee Attrition Prediction using an Explainable FT-Transformer Deep Learning Model
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 627
EP  - 636
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_50
DO  - 10.2991/978-94-6239-654-8_50
ID  - Kanthimathi2026
ER  -