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Early Prediction of Employee Attrition using Data Mining Techniques
4 references·9 citing·48 second hop
Early Prediction of Employee Attrition using Data Mining Techniques (2018)Early Prediction of Employee Attrition us…Designing of customer and employee churn prediction model based on data mining method and neural predictor (2017)Designing of customer and emp…Applications of Data Mining Techniques in Telecom Churn Prediction (2012)Applications of Data Mining T…New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2011)New insights into churn predi…Customer churn prediction using improved balanced random forests (2008)Customer churn prediction usi…Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry (2023)Potential of Artificial Intel…A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review (2023)A decade of research on machi…Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry (2022)Predictive Modeling of Employ…Envisaging Employee Churn Using MCDM and Machine Learning (2022)Envisaging Employee Churn Usi…Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data (2022)Employee attrition prediction…Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms (2022)Automated Prediction of Emplo…Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review (2022)Artificial Intelligence Model…Employee Attrition System Using Tree Based Ensemble Method (2021)Employee Attrition System Usi…A novel deep learning model based on convolutional neural networks for employee churn prediction (2021)A novel deep learning model b…Applying data mining to telecom churn management (2005)Predicting customer churn in mobile networks through analysis of social groups (2010)Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers (2017)Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes (2020)Telecom churn prediction and used techniques, datasets and performance measures: a review (2020)Benchmarking state-of-the-art classification algorithms for credit scoring (2003)Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models (2006)Modeling Customer Lifetime Value (2006)Customer churn prediction in the telecommunication sector using a rough set approach (2016)Retention Futility: Targeting High-Risk Customers Might be Ineffective (2017)A machine learning framework for customer purchase prediction in the non-contractual setting (2018)Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting (2004)Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques (2006)A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry (2016)Customer churn prediction system: a machine learning approach (2021)Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2004)Handling class imbalance in customer churn prediction (2008)Employee churn prediction (2010)A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning (2020)Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models (2023)Prediction of Employee Turnover in Organizations using Machine Learning Algorithms (2016)A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)Customer churn prediction in telecommunication industry using data certainty (2018)A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition (2025)Determining objective weights in multiple criteria problems: The critic method (1995)Meta-Analytic Review of Employee Turnover as a Predictor of Firm Performance (2011)One hundred years of employee turnover theory and research. (2017)Optimising customer retention: An AI-driven personalised pricing approach (2024)Unboxing the mystery: employee churn in the retail industry using machine learning approach (2024)A Formula for the Gini Coefficient (1979)Predicting Employee Attrition Using Machine Learning Techniques (2020)Convolutional Neural Network and Capsule Network Fusion for Effective Attrition Classification (2023)Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI (2024)Employee Retention Analytics: Predictive Modeling for Workforce Stability and Talent Management (2025)Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement (2010)The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis (2021)Artificial intelligence and knowledge sharing: Contributing factors to organizational performance (2022)Sustainable Development of Employee Lifecycle Management in the Age of Global Challenges: Evidence from China, Russia, and Indonesia (2023)Intercultural Communicative Competence in Virtual and Face-to-Face Teamwork: A Quantitative Analysis of Culturally Diverse Teams (2024)Predictive Analytics in Human Resources Management: Evaluating AIHR’s Role in Talent Retention (2025)Emotional intelligence or artificial intelligence– an employee perspective (2019)Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review (2021)Fusing talent horizons: the transformative role of data integration in modern talent management (2024)Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management (2025)Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making (2025)Consumer credit-risk models via machine-learning algorithms (2010)Surveying the forest: A meta‐analysis, moderator investigation, and future‐oriented discussion of the antecedents of voluntary employee turnover (2017)A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective (2021)
this work references cited by second hop

References

  • 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach460 cited
  • 2008Customer churn prediction using improved balanced random forests412 cited
  • 2017Designing of customer and employee churn prediction model based on data mining method and neural predictor61 cited
  • 2012Applications of Data Mining Techniques in Telecom Churn Prediction55 cited

Cited by

  • 2023A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review62 cited
  • 2022Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms59 cited
  • 2021A novel deep learning model based on convolutional neural networks for employee churn prediction52 cited
  • 2023Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry49 cited
  • 2022Envisaging Employee Churn Using MCDM and Machine Learning47 cited
  • 2022Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review45 cited
  • 2022Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data41 cited
  • 2022Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry30 cited
  • 2021Employee Attrition System Using Tree Based Ensemble Method27 cited

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