Prediction of Employee Attrition Using Machine Learning and Ensemble Methods

Qutub, Aseel; Al-Mehmadi, Asmaa; Al-Hssan, Munirah; Aljohani, Ruyan; Alghamdi, Hanan S.

doi:10.18178/ijmlc.2021.11.2.1022

Cited by 46 publications

(24 citation statements)

References 3 publications

(3 reference statements)

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“…The difference from previous studies is in research [7] Random Forest produces a good accuracy value of 0.85, but it produces low precision, recall, and f1 score values. Precision value is 0.60, recall is 0.28, and an f1 score is 0.39.…”

Section: Introductionmentioning

confidence: 72%

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Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification

Sari

Lhaksmana

2022

JoSYC

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Employee attrition is the loss of employees in a company caused by several factors, namely employees resigning, retiring, or other factors. Employee attrition of employees can have a negative impact on a company if it is not handled properly, including decreased productivity. The company also requires more time and effort to recruit and train new employees to fill vacant positions. This attrition prediction aims to help the human resources (HR) department in the company to find out what factors influence the occurrence of employee attrition. This research implements Random Forest while comparing Information Gain, Select K Best, and Recursive Feature Elimination feature selection methods to find which feature selection produces the best performance. The implementation of the aforementioned methods outperforms previous research in terms of accuracy, precision, recall, and f1 scores. In preparing this research, the first author collects data sets, makes programs, and compiles journals. The second author assists the first author in programming and preparing the journal. From the results of the tests that have been carried out, Information Gain produces the highest accuracy value of 89.2%, while Select K Best produces an accuracy value of 87.8% and Recursive Feature Elimination produces an accuracy value of 88.8%.

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Section: Introductionmentioning

confidence: 72%

“…In an HR dataset, the data for each feature generally has a different scale [10]. For example, the age range of employees in the dataset ranges from 20 to 50 years, and earnings range from $1000 to $15,000.…”

Section: Preprocessingmentioning

confidence: 99%

Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification

Sari

Lhaksmana

2022

JoSYC

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“…The automated prediction of employee attrition based on several machine learning models was proposed in this study [15]. The IBM HR employee dataset was utilized for learning model building and model evaluation process.…”

Section: Related Workmentioning

confidence: 99%

“…Learning Type Technique Accuracy Score % [13] 2021 Machine Learning Decision Tree 88 [15] 2021 Machine Learning Decision Tree + Logistic Regression 86 [17] 2021 Machine Learning Logistic Regression 81 [18] 2021 Machine Learning Random Forest 85 [20] 2021 Machine Learning Support Vector Machines 80 Proposed 2022 Machine Learning ETC 93…”

Section: Literature Yearmentioning

confidence: 99%

Predicting Employee Attrition Using Machine Learning Approaches

et al. 2022

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Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the monthly income, hourly rate, job level, and age are the key factors that cause employee attrition. Our proposed approach and research findings help organizations overcome employee attrition by improving the factors that cause attrition.

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“…In [10], authors presented the prediction of employee attrition based on several ML models. The models were developed automatically and achieved high accurate results in prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Aly¹,

Hussein²,

Rahouma³

2022

Computers, Materials &Amp; Continua

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This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

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Prediction of Employee Attrition Using Machine Learning and Ensemble Methods

Cited by 46 publications

References 3 publications

Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification

Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification

Predicting Employee Attrition Using Machine Learning Approaches

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

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