An approach for predicting employee churn by using data mining
dc.contributor.author | Yiğit, İ. O. | |
dc.contributor.author | Shourabizadeh, Hamed | |
dc.date.accessioned | 2018-03-30T12:24:47Z | |
dc.date.available | 2018-03-30T12:24:47Z | |
dc.date.issued | 2017 | |
dc.identifier.isbn | 978-1-5386-1880-6 | |
dc.identifier.uri | http://hdl.handle.net/10679/5794 | |
dc.identifier.uri | http://ieeexplore.ieee.org/document/8090324/?part=1 | |
dc.description | Due to copyright restrictions, the access to the full text of this article is only available via subscription. | |
dc.description.abstract | Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results by calculating the accuracy, precision, recall, and F-measure values of the results. Moreover, we implement a feature selection method on the data and analyze the results with previous ones. The results will lead companies to predict their employees' churn status and consequently help them to reduce their human resource costs. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | Artificial Intelligence and Data Processing Symposium (IDAP), 2017 International | |
dc.rights | restrictedAccess | |
dc.title | An approach for predicting employee churn by using data mining | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.ozuauthor | Shourabizadeh, Hamed | |
dc.identifier.wos | WOS:000426868700164 | |
dc.identifier.doi | 10.1109/IDAP.2017.8090324 | en_US |
dc.subject.keywords | Employee churn prediction | en_US |
dc.subject.keywords | Data analysis | en_US |
dc.subject.keywords | Feature selection | en_US |
dc.subject.keywords | Data mining | en_US |
dc.subject.keywords | Classification | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85039912525 | |
dc.contributor.authorMale | 1 | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff |
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