Machine Learning Based Attrition Prediction

The use of machine learning techniques and models has become widespread with diverse industries using them to glean greater insights from available data. Probabilistic estimation models are used in many cases, often in combination with other methods such as regression and decision trees. The current paper utilizes probabilistic estimation to predict attrition from the human resource database of a company with close to 1500 employees. The initial model is adaptively refined to improve the prediction capability of the model.

[1]  Otmane Aït Mohamed,et al.  Probabilistic High-Level Estimation of Vulnerability and Fault Mitigation of Critical Systems Using Fault-Mitigation Trees (FMTs) , 2019, 2019 IEEE Latin American Test Symposium (LATS).

[2]  Sandeep Yadav,et al.  Early Prediction of Employee Attrition using Data Mining Techniques , 2018, 2018 IEEE 8th International Advance Computing Conference (IACC).

[3]  J Rajanikanth,et al.  PREDICTION OF EMPLOYEE ATTRITION USING DATAMINING , 2018, 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA).

[4]  Giuseppe Notarstefano,et al.  Interaction-Based Distributed Learning in Cyber-Physical and Social Networks , 2020, IEEE Transactions on Automatic Control.

[5]  Kashif Rajpoot,et al.  Predicting Employee Attrition using Machine Learning , 2018, 2018 International Conference on Innovations in Information Technology (IIT).

[6]  Daniel Salmond.  Blind estimation of wireless network topology and throughput , 2019, 2019 53rd Annual Conference on Information Sciences and Systems (CISS).

[7]  Kush R. Varshney,et al.  An Analytics Approach for Proactively Combating Voluntary Attrition of Employees , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[8]  Haiying Shen,et al.  Dynamic Demand Prediction and Allocation in Cloud Service Brokerage , 2019, IEEE Trans. Cloud Comput..

[9]  Mangal Sain,et al.  Prediction of Quality for Different Type of Wine based on Different Feature Sets Using Supervised Machine Learning Techniques , 2019, 2019 21st International Conference on Advanced Communication Technology (ICACT).

[10]  Gernot A. Fink,et al.  Combining Symbolic Reasoning and Deep Learning for Human Activity Recognition , 2019, 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[11]  Anand Nayyar,et al.  Predicting Employee Attrition using XGBoost Machine Learning Approach , 2018, 2018 International Conference on System Modeling & Advancement in Research Trends (SMART).

[12]  Meng Li,et al.  A probabilistic anomaly detection approach for data-driven wind turbine condition monitoring , 2019, CSEE Journal of Power and Energy Systems.

[14]  Jiang Xu-rui,et al.  Application of ensemble learning algorithm in aircraft probabilistic conflict detection of free flight , 2018, 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD).