Prediction of Employee Turn Over Using Random Forest Classifier with Intensive Optimized Pca Algorithm

Ali, Alaeldeen Bader Wild

doi:10.1007/s11277-021-08408-0

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…They also used LDA and PCA to find the relevant characteristics to improve worker turnover prediction rates. This work would be more commendable if it produced any concrete results [29].…”

Section: Literature Reviewmentioning

confidence: 96%

Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction

et al. 2023

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Companies in a wide variety of industries use the customer churn prediction (CCP) process to keep their current clientele happy. Insurance companies need to be able to forecast churn to enhance the potency and functionality of deep learning methods. Deep learning techniques have a significant impact on improving and forecasting customer retention. Numerous studies employ standard machine learning and Deep Learning strategies to enhance customer retention, despite the fact that these strategies have a number of accuracy issues. In light of this need, this piece is dedicated to the development of a stacked bidirectional long short-term memory (SBLSTM) and RNN model for arithmetic optimisation algorithm (AOA) in CCP. The proposed AOA-SBLSTM-RNN model intends to proficiently forecast the occurrence of Customer Churn in the Insurance industry. Initially, the AOA model performs pre-processing to transform the original data into a useful format. In addition, the SBLSTM-RNN model is used to distinguish between churning and non-churning customers. To improve the CCP outcomes of the SBLSTM-RNN model, an optimal Hyperparameters tuning process using Improved Gravitational Search Optimization Algorithm (IGSA) is used in this study. In this work, Three Health Insurance datasets were used to evaluate performance, and four sets of experiments were conducted. The Measures of true churn, false churn, specificity, precision, and accuracy are employed to assess the efficacy of the proposed approach. Experimental result shows that the Ensemble Deep Learning model AOA-SBLSTM-RNN with IGSA produces accuracy value of 97.89% and 97.67% on dataset 2 and dataset 1. which is better and had higher predictability levels in compared with all other models.

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“…They also used LDA and PCA to find the relevant characteristics to improve worker turnover prediction rates. This work would be more commendable if it produced any concrete results [29].…”

Section: Literature Reviewmentioning

confidence: 96%

Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction

et al. 2023

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“…In another study, the Intensive Optimized Principal Component Analysis (PCA) was utilized for feature selection before applying Random Forest (RF) to unveil turnover-associated factors. The data source for this study was an enterprise resource planning (ERP) software database 85 , and the number of variables was fewer than 100. www.nature.com/scientificreports/…”

Section: Similar Workmentioning

confidence: 99%

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

Gadár,

Abonyi

2024

Sci Rep

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In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors.

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“…However, this developed model not considered employee categorization and category-wise retention strategy. Ali [14] proposed a prediction system for classifying employee turnover by using NIOPCA (new intensive optimized principle component analysis) and RFC (random forest classifier). The RFC was used for the classification tasks, and NIOPCA was used for feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Long Short-Term Memory with Recurrent Neural Network and Brownian Motion Butterfly Optimization for Employee Attrition Prediction

2024

IJIES

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Machine learning is a popular technology that continuously supports organizations in their corporate strategies, managerial functions, and team building. There are many areas in which organizations can adopt technologies that will support decision-making. Human resources (HR) have received more attention in recent years as a result of the fact that skilled employees are a significant growth factor and a genuine competitive advantage for the business. Machine learning started to be employed in HR management to help with employee-related decisions after first being introduced to the departments of sales and marketing. The goal is to support decisions that are based on objective analysis of data rather than on subjective factors. In this research, a novel long short-term memory with recurrent neural network (LSTM-RNN) method is implemented for the prediction and classification of employee attrition. To avoid the overfitting issue and make further prediction analysis based on the suitable objective of feature subset selection, the feature selection algorithm termed brownian motion based butterfly optimization algorithm is proposed. The presented model is validated on the international business machines corporation (IBM) HR dataset which consists of 35 features of employees like business travel, age, education, department, daily rate, distance from home, etc. On the dataset of IBM HR, the proposed model achieved accuracy, recall, F score, and precision of 96.68%, 96.62%, 96.62%, 96.64% respectively. The outcomes proved that the proposed method provides better results in the classification of employee attrition.

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Prediction of Employee Turn Over Using Random Forest Classifier with Intensive Optimized Pca Algorithm

Cited by 12 publications

References 18 publications

Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction

Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

A Long Short-Term Memory with Recurrent Neural Network and Brownian Motion Butterfly Optimization for Employee Attrition Prediction

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