Prediction of Employee Turn Over Using Random Forest Classifier with Intensive Optimized Pca Algorithm
Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algo...
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01.08.2021
Springer Nature B.V |
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| ISSN: | 0929-6212, 1572-834X |
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| Abstract | Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algorithms and methodologies for the accurate prediction employee turnover taking place in any organization. First of all preprocessing is done as a precautionary step as always before proceeding with the core part of the proposed work. New Intensive Optimized PCA-Principal Component Analysis is used for feature selection and RFC-Random Forest Classifier is used for the classification purposes to classify accordingly to make the prediction more feasible. For classifying and predicting accurately, a methodology called Random Forest Classifier (RFC) classifier is deployed. The main objective of this work is to utilize Random Forest Classification methodology to break down fundamental purposes lying behind the worker turnover by making use of the information mining technique refer as Intensive Optimized PCA for feature selection. Comparative study taking the proposed novel work with the existing is made for showing the efficiency of this work. The performance of this proposed method was found to perform better with improved yields of ROC, accuracy, precision, recall, and F1 score when compared to other existing methodologies. |
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| AbstractList | Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algorithms and methodologies for the accurate prediction employee turnover taking place in any organization. First of all preprocessing is done as a precautionary step as always before proceeding with the core part of the proposed work. New Intensive Optimized PCA-Principal Component Analysis is used for feature selection and RFC-Random Forest Classifier is used for the classification purposes to classify accordingly to make the prediction more feasible. For classifying and predicting accurately, a methodology called Random Forest Classifier (RFC) classifier is deployed. The main objective of this work is to utilize Random Forest Classification methodology to break down fundamental purposes lying behind the worker turnover by making use of the information mining technique refer as Intensive Optimized PCA for feature selection. Comparative study taking the proposed novel work with the existing is made for showing the efficiency of this work. The performance of this proposed method was found to perform better with improved yields of ROC, accuracy, precision, recall, and F1 score when compared to other existing methodologies. |
| Author | Wild Ali, Alaeldeen Bader |
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| Keywords | ORACLE ERP dataset Feature selection Data mining Classification PCA Algorithm Random Forest |
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| References | Tan, P.-N. (2018) Introduction to data mining: Pearson education India. FrederiksenAJob satisfaction and employee turnover: A firm-level perspectiveGerman Journal of Human Resource Management20173113216110.1177/2397002216683885 SukhijaPBehalSSinghPFace recognition system using genetic algorithmProcedia Computer Science20168541041710.1016/j.procs.2016.05.183 ZhuCIdemudiaCUFengWImproved logistic regression model for diabetes prediction by integrating PCA and K-means techniquesInformatics in Medicine Unlocked20191710017910.1016/j.imu.2019.100179 LiuFTsoKYangYGuanJMultilevel analysis of employee satisfaction on commitment to organizational culture: Case study of chinese state-owned enterprisesMathematical and Computational Applications2017224610.3390/mca22040046 Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B. & Zhu, X. (2018) Employee turnover prediction with machine learning: A reliable approach. In Proceedings of SAI intelligent systems conference, (pp. 737–758). PuXFanKChenXJiLZhouZFacial expression recognition from image sequences using twofold random forest classifierNeurocomputing20151681173118010.1016/j.neucom.2015.05.005 ValleMARuzGAMasíasVHUsing self-organizing maps to model turnover of sales agents in a call centerApplied Soft Computing20176076377410.1016/j.asoc.2017.03.011 Saleem, D. I., Ahmed, R., & Saleem, N. (2016) "Mediating role of work exhaustion: The missing linchpin to address employee's turnover," Saleem I., Ahmed R. & Saleem, (pp. 156–173) BelgiuMDrăguţLRandom forest in remote sensing: A review of applications and future directionsISPRS Journal of Photogrammetry and Remote Sensing2016114243110.1016/j.isprsjprs.2016.01.011 YiğitİOShourabizadehHAn approach for predicting employee churn by using data miningIn International Artificial Intelligence and Data Processing Symposium (IDAP)2017201714 IerodiakonouCStavrouEFlexitime and employee turnover: the polycontextuality of regulation as cross-national institutional contingencyThe International Journal of Human Resource Management2017283003302610.1080/09585192.2017.1362658 HuangW-RSuC-HThe mediating role of job satisfaction in the relationship between job training satisfaction and turnover intentionsIndustrial and Commercial Training201648425210.1108/ICT-04-2015-0029 AjitPPrediction of employee turnover in organizations using machine learning algorithmsAlgorithms201645C5 Islam, M. K., Alam, M. M., Islam, M. B., Mohiuddin, K., Das, A. K., & Kaonain, M. S. (2018) An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model. In International Conference on Advances in Computing and Data Sciences, (pp. 269–278). KarandeSShyamalaLYu-ChenHuTiwariShaileshMishraKrishn KTrivediMunesh CPrediction of employee turnover using ensemble learningAmbient Communications and Computer Systems2019SingaporeSpringer UllahIRazaBMalikAKImranMIslamSUKimSWA churn prediction model using random forest: Analysis of machine learning techniques for churn prediction and factor identification in telecom sectorIEEE Access20197601346014910.1109/ACCESS.2019.2914999 XiaJLiaoWChanussotJDuPSongGPhilipsWImproving random forest with ensemble of features and semisupervised feature extractionIEEE Geoscience and Remote Sensing Letters2015121471147510.1109/LGRS.2015.2409112 FarnaazNJabbarMRandom forest modeling for network intrusion detection systemProcedia Computer Science20168921321710.1016/j.procs.2016.06.047 DwivediAKAnalysis of computational intelligence techniques for diabetes mellitus predictionNeural Computing and Applications2018303837384510.1007/s00521-017-2969-9 KoddenBRoelofsJPsychological contract as a mediator of the leadership-turnover intentions relationshipJournal of Organizational Psychology201919293102 MaseticZSubasiACongestive heart failure detection using random forest classifierComputer methods and programs in biomedicine2016130546410.1016/j.cmpb.2016.03.020 Frierson, J. & Si, D.(2018) Who’s next: Evaluating attrition with machine learning algorithms and survival analysis, In International Conference on Big Data, (pp. 251–259). El-rayesNSmithMTaylorSMAn explicative and predictive study of employee attrition using tree-based modelsSSRN Electronic Journal201910.2139/ssrn.3397445 P Sukhija (8408_CR14) 2016; 85 8408_CR23 W-R Huang (8408_CR5) 2016; 48 Z Masetic (8408_CR9) 2016; 130 İO Yiğit (8408_CR24) 2017; 2017 N El-rayes (8408_CR20) 2019 8408_CR3 8408_CR1 MA Valle (8408_CR16) 2017; 60 C Ierodiakonou (8408_CR21) 2017; 28 B Kodden (8408_CR22) 2019; 19 C Zhu (8408_CR15) 2019; 17 I Ullah (8408_CR8) 2019; 7 AK Dwivedi (8408_CR2) 2018; 30 X Pu (8408_CR13) 2015; 168 S Karande (8408_CR6) 2019 M Belgiu (8408_CR10) 2016; 114 F Liu (8408_CR17) 2017; 22 P Ajit (8408_CR7) 2016; 4 J Xia (8408_CR12) 2015; 12 8408_CR19 A Frederiksen (8408_CR4) 2017; 31 8408_CR18 N Farnaaz (8408_CR11) 2016; 89 |
| References_xml | – reference: Tan, P.-N. (2018) Introduction to data mining: Pearson education India. – reference: FrederiksenAJob satisfaction and employee turnover: A firm-level perspectiveGerman Journal of Human Resource Management20173113216110.1177/2397002216683885 – reference: Islam, M. K., Alam, M. M., Islam, M. B., Mohiuddin, K., Das, A. K., & Kaonain, M. S. (2018) An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model. In International Conference on Advances in Computing and Data Sciences, (pp. 269–278). – reference: Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B. & Zhu, X. (2018) Employee turnover prediction with machine learning: A reliable approach. In Proceedings of SAI intelligent systems conference, (pp. 737–758). – reference: SukhijaPBehalSSinghPFace recognition system using genetic algorithmProcedia Computer Science20168541041710.1016/j.procs.2016.05.183 – reference: BelgiuMDrăguţLRandom forest in remote sensing: A review of applications and future directionsISPRS Journal of Photogrammetry and Remote Sensing2016114243110.1016/j.isprsjprs.2016.01.011 – reference: KarandeSShyamalaLYu-ChenHuTiwariShaileshMishraKrishn KTrivediMunesh CPrediction of employee turnover using ensemble learningAmbient Communications and Computer Systems2019SingaporeSpringer – reference: LiuFTsoKYangYGuanJMultilevel analysis of employee satisfaction on commitment to organizational culture: Case study of chinese state-owned enterprisesMathematical and Computational Applications2017224610.3390/mca22040046 – reference: KoddenBRoelofsJPsychological contract as a mediator of the leadership-turnover intentions relationshipJournal of Organizational Psychology201919293102 – reference: YiğitİOShourabizadehHAn approach for predicting employee churn by using data miningIn International Artificial Intelligence and Data Processing Symposium (IDAP)2017201714 – reference: Saleem, D. I., Ahmed, R., & Saleem, N. (2016) "Mediating role of work exhaustion: The missing linchpin to address employee's turnover," Saleem I., Ahmed R. & Saleem, (pp. 156–173) – reference: ZhuCIdemudiaCUFengWImproved logistic regression model for diabetes prediction by integrating PCA and K-means techniquesInformatics in Medicine Unlocked20191710017910.1016/j.imu.2019.100179 – reference: El-rayesNSmithMTaylorSMAn explicative and predictive study of employee attrition using tree-based modelsSSRN Electronic Journal201910.2139/ssrn.3397445 – reference: IerodiakonouCStavrouEFlexitime and employee turnover: the polycontextuality of regulation as cross-national institutional contingencyThe International Journal of Human Resource Management2017283003302610.1080/09585192.2017.1362658 – reference: XiaJLiaoWChanussotJDuPSongGPhilipsWImproving random forest with ensemble of features and semisupervised feature extractionIEEE Geoscience and Remote Sensing Letters2015121471147510.1109/LGRS.2015.2409112 – reference: ValleMARuzGAMasíasVHUsing self-organizing maps to model turnover of sales agents in a call centerApplied Soft Computing20176076377410.1016/j.asoc.2017.03.011 – reference: UllahIRazaBMalikAKImranMIslamSUKimSWA churn prediction model using random forest: Analysis of machine learning techniques for churn prediction and factor identification in telecom sectorIEEE Access20197601346014910.1109/ACCESS.2019.2914999 – reference: PuXFanKChenXJiLZhouZFacial expression recognition from image sequences using twofold random forest classifierNeurocomputing20151681173118010.1016/j.neucom.2015.05.005 – reference: Frierson, J. & Si, D.(2018) Who’s next: Evaluating attrition with machine learning algorithms and survival analysis, In International Conference on Big Data, (pp. 251–259). – reference: FarnaazNJabbarMRandom forest modeling for network intrusion detection systemProcedia Computer Science20168921321710.1016/j.procs.2016.06.047 – reference: MaseticZSubasiACongestive heart failure detection using random forest classifierComputer methods and programs in biomedicine2016130546410.1016/j.cmpb.2016.03.020 – reference: HuangW-RSuC-HThe mediating role of job satisfaction in the relationship between job training satisfaction and turnover intentionsIndustrial and Commercial Training201648425210.1108/ICT-04-2015-0029 – reference: DwivediAKAnalysis of computational intelligence techniques for diabetes mellitus predictionNeural Computing and Applications2018303837384510.1007/s00521-017-2969-9 – reference: AjitPPrediction of employee turnover in organizations using machine learning algorithmsAlgorithms201645C5 – year: 2019 ident: 8408_CR20 publication-title: SSRN Electronic Journal doi: 10.2139/ssrn.3397445 – volume: 48 start-page: 42 year: 2016 ident: 8408_CR5 publication-title: Industrial and Commercial Training doi: 10.1108/ICT-04-2015-0029 – ident: 8408_CR18 doi: 10.1007/978-3-319-94301-5_19 – volume: 7 start-page: 60134 year: 2019 ident: 8408_CR8 publication-title: IEEE Access doi: 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| Title | Prediction of Employee Turn Over Using Random Forest Classifier with Intensive Optimized Pca Algorithm |
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