An ensemble learning approach to predict employees' preference for E-working in the post-pandemic world
By: Abesiri, S. A. D. D
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Contributor(s): Rupasingha, R A H M.
Publisher: Hyderabad IUP Publications 2022Edition: Vol.18(4), Dec.Description: 7-24p.Subject(s): Computer EngineeringOnline resources: Click here
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IUP journal of information technologySummary: The Covid-19 pandemic has forced a large segment of the global workforce to shift to e-working. The pandemic has convinced many organizations that e-working has benefits for a successful business. As a result, it is critical to identify employees' suggestions and evaluate their motivation to continue the e-working concept in the post-pandemic world. The study was conducted by randomly surveying employees using various Machine Learning algorithms, including Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and logistic regression. The ensembling algorithm uses 66% of the percentage split method in the Waikato Environment for Knowledge Analysis (WEKA) tool. Accuracy, precision, recall, f-measure values and error rates were used to compare the results. The ensemble learning algorithm shows the best results with 90% accuracy, making it easier to predict employees' preference for e-working and accordingly take decisions.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0996 |
The Covid-19 pandemic has forced a large segment of the global workforce to shift to e-working. The pandemic has convinced many organizations that e-working has benefits for a successful business. As a result, it is critical to identify employees' suggestions and evaluate their motivation to continue the e-working concept in the post-pandemic world. The study was conducted by randomly surveying employees using various Machine Learning algorithms, including Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and logistic regression. The ensembling algorithm uses 66% of the percentage split method in the Waikato Environment for Knowledge Analysis (WEKA) tool. Accuracy, precision, recall, f-measure values and error rates were used to compare the results. The ensemble learning algorithm shows the best results with 90% accuracy, making it easier to predict employees' preference for e-working and accordingly take decisions.
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