Predicting the Perceived Employee Tendency of Leaving an Organization Using SVM and Naive Bayes Techniques

Emmanuel-Okereke, Ijeoma Lilian; Anigbogu, Sylvanus Okwudili

doi:10.4236/oalib.1108497

Cited by 4 publications

(2 citation statements)

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“…ere are many other machine learning approaches, including support vector machines (SVM) and random forest (RF) [51][52][53], each of which has its areas of applicability [54]. To further investigate the optimization performance of the proposed method, it was compared with other machine learning methods, including RF and SVM methods.…”

Section: Operating Resultmentioning

confidence: 99%

Prediction of the Normalized COVID-19 Epidemic Prevention Costs of Construction Projects Based on an Optimized Neural Network

Yan

Feng

Chen

2022

Mathematical Problems in Engineering

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During the COVID-19 epidemic, the Chinese central government adopted a dynamic clearing prevention and control strategy. Meanwhile, most local governments issued policies to incorporate normal epidemic prevention costs into the costs of construction projects. However, there are few provisions on how to determine the calculation standards for these costs. To accurately predict the normalized epidemic prevention costs of construction projects from different aspects, the relevant factors that affect epidemic prevention costs are investigated and an optimized neural network prediction method that can effectively eliminate abnormal data with a too large deviation is proposed. The results show that compared with the traditional backpropagation (BP) neural network and BP neural networks optimized by genetic algorithm, the error of the optimized neural network achieves a smaller error in predicting the normalized epidemic prevention costs of construction projects (the average error of the traditional BP neural networks is 6%). Meanwhile, among the factors that affect epidemic prevention costs, total investment, project category, and construction scale have the greatest impact. Based on the research results, this paper proposes pricing suggestions and corresponding management solutions for the epidemic prevention costs of construction projects, which will be helpful to project managers.

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Section: Operating Resultmentioning

confidence: 99%

Prediction of the Normalized COVID-19 Epidemic Prevention Costs of Construction Projects Based on an Optimized Neural Network

Yan

Feng

Chen

2022

Mathematical Problems in Engineering

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“…Zhao et al (2019) [9] and Kovvuri & Dommeti (2022) [10] discussed the dataset-specific effectiveness of various algorithms, often identifying XGBoost as a strong contender. Additionally, Emmanuel-Okereke & Anigbogu (2022) [11] , along with R. Punnoose & Ajit (2016) [12] , highlighted the potential of Naive Bayes and SVM techniques, respectively, in turnover prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Study of Employee Turnover Analysis Methods

Singh,

Jain,

Ranjan

2024

Int. J. Res. Publ. Rev.

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In the rapidly evolving landscape of human resource management, understanding and mitigating employee turnover has emerged as a paramount challenge. This research delves into a comparative analysis of various machine learning algorithms applied to the same dataset over several research papers, with the objective of identifying the most effective method for predicting employee turnover. Through a meticulous evaluation based on accuracy, F1 score, and ROC value, this study aspires to identify the best methods for predicting employee turnover.This study highlights the Artificial Neural Network (ANN) as the top algorithm for predicting employee turnover due to its outstanding performance across multiple metrics, while XGBoost and LGBM Regression are also identified to be highly effective, providing a good mix of efficiency, interpretability, and reliability.• Performance Metrics: The evaluation of algorithms is based on accuracy, F1 score, and ROC AUC score. While these metrics provide a robust framework for comparison, they might not capture all aspects of model performance relevant to turnover prediction, such as calibration and the cost-sensitive nature of false positives versus false negatives in real-world HR decision-making.

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Leveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics

Taner,

Areta Hızıroğlu,

Hızıroğlu

2024

jista

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Employee turnover is a critical challenge for organizations, leading to significant costs and disruptions. This study aims to leverage Machine Learning (ML) techniques within the framework of Human Resources Analytics (HRA) to predict employee turnover effectively. The research evaluates and compares the performance of six widely used models: Decision Trees, Support Vector Machines (SVM), Logistic Regression, Random Forest, XGBoost, and Artificial Neural Networks. These models were implemented using the R programming language on an open-source dataset from IBM. The methodology involved data preprocessing, splitting into training, validation and testing sets, model training, and performance evaluation using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and ROC-AUC. The results indicate that the Logistic Regression model outperformed the other models, achieving high accuracy and a good F1-score. The study concludes by emphasizing the importance of HRA and ML techniques in predicting and managing employee turnover, while discussing limitations such as class imbalance and the need for more rigorous performance evaluation. Future research directions include exploring alternative models, feature selection techniques, and addressing class imbalance.

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Predicting the Perceived Employee Tendency of Leaving an Organization Using SVM and Naive Bayes Techniques

Cited by 4 publications

References 0 publications

Prediction of the Normalized COVID-19 Epidemic Prevention Costs of Construction Projects Based on an Optimized Neural Network

Prediction of the Normalized COVID-19 Epidemic Prevention Costs of Construction Projects Based on an Optimized Neural Network

Comparative Study of Employee Turnover Analysis Methods

Leveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics

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