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Çalışan Yıpranmasının ve Yıpranmaya Neden Olan Faktörlerin Tahmininde Makine Öğrenimi Yaklaşımı

Yıl 2021, Cilt: 36 Sayı: 4, 913 - 928, 29.12.2021
https://doi.org/10.21605/cukurovaumfd.1040487

Öz

İşletmeler için oldukça önemli olan insan kaynağının yıpranmasının ve yıpranmanın doğal sonucu olan işten ayrılmanın önüne geçmek amacıyla yapılan bu çalışmada, yıpranmaya neden olan faktörler tahmine dayalı analitik tekniklerinden biri olan makine öğrenmesi yöntemleri kullanılarak belirlenmeye çalışılmıştır. Analiz için örnek veri seti IBM şirketi Watson Analytics programı kapsamında sunulan bir veri tabanından alınmıştır. Veri seti, 1470 adet çalışanın 30 farklı özniteliğini içermektedir. Çalışmada, tahmin başarısını değerlendirmek amacıyla yedi farklı makine öğrenmesi algoritması kullanılmıştır. Yıpranmaya neden olan faktörlerin tespitinde ise kazanç oranı yaklaşımı tercih edilmiştir. Çalışmanın kilit noktası, bootstrap tekniği ile yeniden örnekleme yapılarak sınıfların örnek sayılarının dengelenmesidir. Sonuç olarak, yeniden örnekleme ile makine öğrenmesi yöntemlerinin anlamlı sonuçlar vermesi sağlanmış ve tahmin doğruluk performansı, kör test yapılmasına rağmen %80’ler seviyesine ulaşmıştır. Kazanç oranı ile yapılan öncelik sıralamasında ilk 20’de yer alan özelliğin, yıpranmaya neden olan öncelikli faktörler olabileceği belirlenmiştir.

Kaynakça

  • 1. Sridhar, G.V., Vetrivel, S., Venugopal, S., 2018. Employee Attrition and Employee Retention-challenges & Suggestions. 2018 International Conference on Economic Transformation with Inclusive Growth-2018, Chennai, India, 1-16.
  • 2. Alao, D., Adeyemo, A.B., 2013. Analyzing Employee Attrition Using Decision Tree Algorithms. Computing, Information Systems & Development Informatics Journal, 4(1), 17-28.
  • 3. Srivastava, D.K., Nair, P., 2017. Employee Attrition Analysis Using Predictive Techniques. 2017 International Conference on Information and Communication Technology for Intelligent Systems, Ahmedabad, India, 293-300.
  • 4. Raman, R., Bhattacharya, S., Pramod, D., 2019. Predict Employee Attrition by Using Predictive Analytics. Benchmarking: An International Journal, 26(1), 2-18.
  • 5. Gandomi, A., Haider, M., 2015. Beyond the Hype: Big Data Concepts, Methods and Analytics. International Journal of Information Management, 35(2), 137-144.
  • 6. Zhao, W., Pu, S., Jiang, D., 2020. A Human Resource Allocation Method for Business Processes Using Team Faultlines. Applied Intelligence, 50, 2887-2900.
  • 7. Yedida, R., Reddy, R., Vahi, R., Jana, R.J., Gv, A., Kulkarni, D., 2018. Employee Attrition Prediction, arXiv:1806.10480, https://arxiv.org/ ftp/arxiv/papers/1806/1806.10480.pdf
  • 8. Punnoose, R., Ajit, P., 2016. Prediction of Employee Turnover in Organizations Using Machine Learning Algorithms. International Journal of Advanced Research in Artificial Intelligence, 5(9), 22-26.
  • 9. Shankar, R.S., Rajanikanth, J., Sivaramaraju, V.V., Murthy, K.VSSR., 2018. Prediction of Employee Attrition Using Datamining. 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 335-342.
  • 10. Çelik, U., 2019. Estimation of Employee Attrition in Business Life Balance with Data Mining Methods. Journal of Management and Economics Research, 17(1), 63-76.
  • 11. Sevilla, J., 1997. Importance of Input Data Normalization for the Application of Neural Networks to Complex Industrial Problems. IEEE Transactions on Nuclear Science, 44(3), 1464 – 1468.
  • 12. Zhang, Y-P., Qiqige, W., Zheng, W., Liu, S., Zhao, C., 2016. gDNA-Prot: Predict DNA-Binding Proteins by Employing Support Vector Machine and a Novel Numerical Characterization of Protein Sequence. Journal of Theoretical Biology, 406, 8-16.
  • 13. Christo, V.R.E., Nehemiah, H.K., Minu, B., Kannan, A., 2019. Correlation-based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network. Computational and Mathematical Methods in Medicine, 7398307, 1-17.
  • 14. Wang, Z., Fu, Y., Huang, T.S., 2019. Signal Processing. Deep Learning Through Sparse and Low-rank Modeling, San Diego, USA: Academic Press, 121-142.
  • 15. Duda, R.O., Hart, P.E., Stork, D.G., 2000. Pattern Classification. John Wiley & Sons, New York, USA, 688.
  • 16. Raitoharju, J., Kiranyaz, S., Gabbouj, M., 2016. Training Radial Basis Function Neural Networks for Classification via Class-specific Clustering. IEEE Transactions on Neural Networks and Learnıng Systems, 27 12 , 2458-2471.
  • 17. Schwenker, F., Kestler, H.A., Palm, G., 2001. Three Learning Phases for Radial-basis-function Networks. Neural Networks, 14, 439-458.
  • 18. Faris, H., Aljarah, I., Mirjalili, S., 2017. Evolving Radial Basis Function Networks Using Moth–flame Optimizer. Samui, P., Sekhar, S., Balas, V.E., (Ed.), Handbook of Neural Computation, San Diego, USA: Academic Press, 537-550.
  • 19. Cortes, C., Vapnik, V., 1995. Support-Vector Networks. Machine Learning, 20, 273-297.
  • 20. Battineni, G., Chintalapudi, N., Amenta, F., 2019. Machine Learning in Medicine: Performance Calculation of Dementia Prediction by Support Vector Machines (SVM). Informatics in Medicine Unlocked, 16:100200, 1-8.
  • 21. Awad, M., Khanna, R., 2015. Support Vector Machines for Classification. Awad, M., Khanna, R., (Ed.). Efficient Learning Machines, Berkeley, CA: Apress, 39-66.
  • 22. Ibrikçi, T., Üstün, D., Ersöz Kaya, I., 2012. Diagnosis of Several Diseases by Using Combined Kernels with Support Vector Machine. Journal of Medical Systems, 36(3), 1831-1840.
  • 23. Öztürk, G., Çimen, E., 2019. Polyhedral Conic Kernel-like Functions for SVMs, Turkish Journal of Electrical Engineering & Computer Sciences, 27, 1172-1180.
  • 24. Breiman, L., 2001. Random Forests. Machine Learning, 45(1), 5-32.
  • 25. Pal, M., 2005. Random Forest Classifier for Remote Sensing Classification. International Journal of Remote Sensing, 26(1), 217-222.
  • 26. Winham, S.J., Freimuth, R.R., Biernacka, J.M., 2013. A Weighted Random Forests Approach to Improve Predictive Performance. Statistical Analysis and Data Mining, 6(6), 496-505.
  • 27. Chan, A.P.C., Wong, F.K.W., Hon, C.K.H., Choi, T.N.Y., 2018. A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work. International Journal of Environmental Research and Public Health, 15(11):2496, 1-19.
  • 28. Carson, E., Cobelli, C., 2014. Modelling Methodology for Physiology and Medicine. Elseiver, Waltham, USA, 588.
  • 29. Ruz, G.A., Araya-Diaz, P., 2018. Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers. Complexity, (4075656), 1-14.
  • 30. Fix, E., Hodges, J.L., 1951. Discriminatory Analysis-nonparametric Discrimination: Consistency Properties. Project No. 2-49-004, Report No. 4, Contract No. AF 41(128)-31, USAF School of Aviation, Randolph Field, Texas.
  • 31. Lu, L., Zhu, Z., 2014. Prediction Model for Eating Property of Indica Rice. Journal of Food Quality, 37, 274-280.
  • 32. Cohen, W.W., 1995. Fast Effective Rule Induction. 1995 Twelfth International Conference on Machine Learning, California, 115-123.
  • 33. Rezapour, M., Zadeh, M.K., Sepehri, M.M., 2013. Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients. Computational and Mathematical Methods in Medicine, 2013 (Article ID: 830745), 1-8.
  • 34. Du, J., 2010. Iterative Optimization of Rule Sets, Master’s Thesis. Technische Universitat Darmstadt, Fachbereich Informatik, Darmstadt, 72.
  • 35. Witten, I.H., Frank, E., 2005. Data Mining: Practical Machine Learning Tools and Techniques. Elsevier Inc., San Francisco, USA, 525.
  • 36. Chen, J., Li, Q., Wang, H., Deng, M., 2020. A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta. China, International Journal of Environmental Research and Public Health, 17(1), 49, 1-21.
  • 37. Kaya, I.E., Ibrikci, T., Ersoy, O.K., 2011. Prediction of Disorder with New Computational Tool: BVDEA. Expert Systems with Applications, 38, 14451-14459.
  • 38. Carrington, A.M., Fieguth, P.W., Qazi, H., Holzinger, A., Chen, H.H., Mayr, F., Manuel, D.G., 2020. A New Concordant Partial AUC and Partial C Statistics for Imbalanced Data in the Evaluation of Machine Learning Algorithms. BMC Medical Informatics and Decision Making, 20 (4), 1-12.
  • 39. Yang, Z.R., Thomson, R., McNeil, P., Esnouf, R.M., 2005. RONN: The Bio-Basis Function Neural Network Technique Applied to the Detection of Natively Disordered Regions in Proteins. Bioinformatics, 21, 3369–3376.
  • 40. Alduayj, S.S., Rajpoot, K., 2018. Predicting Employee Attrition Using Machine Learning. IIT 2018: 13th International Conference on Innovations in Information Technology, Al Ain, United Arab Emirates, 93-98.
  • 41. Bhuva, K., Srivastava, K., 2018. Comparative Study of the Machine Learning Techniques for Predicting the Employee Attrition. International Journal of Research and Analytical Reviews, 5(3), 568-577.
  • 42. Paredes, M., 2018. A Case Study on Reducing Auto Insurance Attrition with Econometrics, Machine Learning, and A/B Testing. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 410-414.
  • 43. Sukhadiya, J., Kapadia, H., D’silva, M., 2018. Employee Attrition Prediction Using Data Mining Techniques. International Journal of Management, Technology And Engineering, 8(X), 2882-2888.

Machine Learning Approach for Predicting Employee Attrition and Factors Leading to Attrition

Yıl 2021, Cilt: 36 Sayı: 4, 913 - 928, 29.12.2021
https://doi.org/10.21605/cukurovaumfd.1040487

Öz

In this study that aims to prevent the attrition of human resource which is so important for enterprises, as well as to prevent the leave of employment which is the natural result of such attrition, employee attrition and factors causing attrition are tried to be determined by predictive analytics approaches. The sample dataset which contains 30 different attributes of 1470 employees was obtained for the analysis from a database provided by IBM Watson Analytics. In the study, seven different machine learning algorithms were used to evaluate the prediction achievements. The gain ratio approach was preferred in determining the factors causing attrition. The key point of the study was to cope with the imbalanced data through resampling with bootstrapping. Thereby, even in the blind test, prospering prediction performances reaching up to 80% accuracy were achieved in robust specificity without sacrificing sensitivity. Therewithal, the effective factors causing attrition were investigated in the study and it was concluded that the first 20 attributes ranked according to their gain ratio were sufficient in explaining attrition.

Kaynakça

  • 1. Sridhar, G.V., Vetrivel, S., Venugopal, S., 2018. Employee Attrition and Employee Retention-challenges & Suggestions. 2018 International Conference on Economic Transformation with Inclusive Growth-2018, Chennai, India, 1-16.
  • 2. Alao, D., Adeyemo, A.B., 2013. Analyzing Employee Attrition Using Decision Tree Algorithms. Computing, Information Systems & Development Informatics Journal, 4(1), 17-28.
  • 3. Srivastava, D.K., Nair, P., 2017. Employee Attrition Analysis Using Predictive Techniques. 2017 International Conference on Information and Communication Technology for Intelligent Systems, Ahmedabad, India, 293-300.
  • 4. Raman, R., Bhattacharya, S., Pramod, D., 2019. Predict Employee Attrition by Using Predictive Analytics. Benchmarking: An International Journal, 26(1), 2-18.
  • 5. Gandomi, A., Haider, M., 2015. Beyond the Hype: Big Data Concepts, Methods and Analytics. International Journal of Information Management, 35(2), 137-144.
  • 6. Zhao, W., Pu, S., Jiang, D., 2020. A Human Resource Allocation Method for Business Processes Using Team Faultlines. Applied Intelligence, 50, 2887-2900.
  • 7. Yedida, R., Reddy, R., Vahi, R., Jana, R.J., Gv, A., Kulkarni, D., 2018. Employee Attrition Prediction, arXiv:1806.10480, https://arxiv.org/ ftp/arxiv/papers/1806/1806.10480.pdf
  • 8. Punnoose, R., Ajit, P., 2016. Prediction of Employee Turnover in Organizations Using Machine Learning Algorithms. International Journal of Advanced Research in Artificial Intelligence, 5(9), 22-26.
  • 9. Shankar, R.S., Rajanikanth, J., Sivaramaraju, V.V., Murthy, K.VSSR., 2018. Prediction of Employee Attrition Using Datamining. 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 335-342.
  • 10. Çelik, U., 2019. Estimation of Employee Attrition in Business Life Balance with Data Mining Methods. Journal of Management and Economics Research, 17(1), 63-76.
  • 11. Sevilla, J., 1997. Importance of Input Data Normalization for the Application of Neural Networks to Complex Industrial Problems. IEEE Transactions on Nuclear Science, 44(3), 1464 – 1468.
  • 12. Zhang, Y-P., Qiqige, W., Zheng, W., Liu, S., Zhao, C., 2016. gDNA-Prot: Predict DNA-Binding Proteins by Employing Support Vector Machine and a Novel Numerical Characterization of Protein Sequence. Journal of Theoretical Biology, 406, 8-16.
  • 13. Christo, V.R.E., Nehemiah, H.K., Minu, B., Kannan, A., 2019. Correlation-based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network. Computational and Mathematical Methods in Medicine, 7398307, 1-17.
  • 14. Wang, Z., Fu, Y., Huang, T.S., 2019. Signal Processing. Deep Learning Through Sparse and Low-rank Modeling, San Diego, USA: Academic Press, 121-142.
  • 15. Duda, R.O., Hart, P.E., Stork, D.G., 2000. Pattern Classification. John Wiley & Sons, New York, USA, 688.
  • 16. Raitoharju, J., Kiranyaz, S., Gabbouj, M., 2016. Training Radial Basis Function Neural Networks for Classification via Class-specific Clustering. IEEE Transactions on Neural Networks and Learnıng Systems, 27 12 , 2458-2471.
  • 17. Schwenker, F., Kestler, H.A., Palm, G., 2001. Three Learning Phases for Radial-basis-function Networks. Neural Networks, 14, 439-458.
  • 18. Faris, H., Aljarah, I., Mirjalili, S., 2017. Evolving Radial Basis Function Networks Using Moth–flame Optimizer. Samui, P., Sekhar, S., Balas, V.E., (Ed.), Handbook of Neural Computation, San Diego, USA: Academic Press, 537-550.
  • 19. Cortes, C., Vapnik, V., 1995. Support-Vector Networks. Machine Learning, 20, 273-297.
  • 20. Battineni, G., Chintalapudi, N., Amenta, F., 2019. Machine Learning in Medicine: Performance Calculation of Dementia Prediction by Support Vector Machines (SVM). Informatics in Medicine Unlocked, 16:100200, 1-8.
  • 21. Awad, M., Khanna, R., 2015. Support Vector Machines for Classification. Awad, M., Khanna, R., (Ed.). Efficient Learning Machines, Berkeley, CA: Apress, 39-66.
  • 22. Ibrikçi, T., Üstün, D., Ersöz Kaya, I., 2012. Diagnosis of Several Diseases by Using Combined Kernels with Support Vector Machine. Journal of Medical Systems, 36(3), 1831-1840.
  • 23. Öztürk, G., Çimen, E., 2019. Polyhedral Conic Kernel-like Functions for SVMs, Turkish Journal of Electrical Engineering & Computer Sciences, 27, 1172-1180.
  • 24. Breiman, L., 2001. Random Forests. Machine Learning, 45(1), 5-32.
  • 25. Pal, M., 2005. Random Forest Classifier for Remote Sensing Classification. International Journal of Remote Sensing, 26(1), 217-222.
  • 26. Winham, S.J., Freimuth, R.R., Biernacka, J.M., 2013. A Weighted Random Forests Approach to Improve Predictive Performance. Statistical Analysis and Data Mining, 6(6), 496-505.
  • 27. Chan, A.P.C., Wong, F.K.W., Hon, C.K.H., Choi, T.N.Y., 2018. A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work. International Journal of Environmental Research and Public Health, 15(11):2496, 1-19.
  • 28. Carson, E., Cobelli, C., 2014. Modelling Methodology for Physiology and Medicine. Elseiver, Waltham, USA, 588.
  • 29. Ruz, G.A., Araya-Diaz, P., 2018. Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers. Complexity, (4075656), 1-14.
  • 30. Fix, E., Hodges, J.L., 1951. Discriminatory Analysis-nonparametric Discrimination: Consistency Properties. Project No. 2-49-004, Report No. 4, Contract No. AF 41(128)-31, USAF School of Aviation, Randolph Field, Texas.
  • 31. Lu, L., Zhu, Z., 2014. Prediction Model for Eating Property of Indica Rice. Journal of Food Quality, 37, 274-280.
  • 32. Cohen, W.W., 1995. Fast Effective Rule Induction. 1995 Twelfth International Conference on Machine Learning, California, 115-123.
  • 33. Rezapour, M., Zadeh, M.K., Sepehri, M.M., 2013. Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients. Computational and Mathematical Methods in Medicine, 2013 (Article ID: 830745), 1-8.
  • 34. Du, J., 2010. Iterative Optimization of Rule Sets, Master’s Thesis. Technische Universitat Darmstadt, Fachbereich Informatik, Darmstadt, 72.
  • 35. Witten, I.H., Frank, E., 2005. Data Mining: Practical Machine Learning Tools and Techniques. Elsevier Inc., San Francisco, USA, 525.
  • 36. Chen, J., Li, Q., Wang, H., Deng, M., 2020. A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta. China, International Journal of Environmental Research and Public Health, 17(1), 49, 1-21.
  • 37. Kaya, I.E., Ibrikci, T., Ersoy, O.K., 2011. Prediction of Disorder with New Computational Tool: BVDEA. Expert Systems with Applications, 38, 14451-14459.
  • 38. Carrington, A.M., Fieguth, P.W., Qazi, H., Holzinger, A., Chen, H.H., Mayr, F., Manuel, D.G., 2020. A New Concordant Partial AUC and Partial C Statistics for Imbalanced Data in the Evaluation of Machine Learning Algorithms. BMC Medical Informatics and Decision Making, 20 (4), 1-12.
  • 39. Yang, Z.R., Thomson, R., McNeil, P., Esnouf, R.M., 2005. RONN: The Bio-Basis Function Neural Network Technique Applied to the Detection of Natively Disordered Regions in Proteins. Bioinformatics, 21, 3369–3376.
  • 40. Alduayj, S.S., Rajpoot, K., 2018. Predicting Employee Attrition Using Machine Learning. IIT 2018: 13th International Conference on Innovations in Information Technology, Al Ain, United Arab Emirates, 93-98.
  • 41. Bhuva, K., Srivastava, K., 2018. Comparative Study of the Machine Learning Techniques for Predicting the Employee Attrition. International Journal of Research and Analytical Reviews, 5(3), 568-577.
  • 42. Paredes, M., 2018. A Case Study on Reducing Auto Insurance Attrition with Econometrics, Machine Learning, and A/B Testing. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 410-414.
  • 43. Sukhadiya, J., Kapadia, H., D’silva, M., 2018. Employee Attrition Prediction Using Data Mining Techniques. International Journal of Management, Technology And Engineering, 8(X), 2882-2888.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İrem Ersöz Kaya Bu kişi benim 0000-0001-5553-3881

Oya Korkmaz Bu kişi benim 0000-0003-4570-803X

Yayımlanma Tarihi 29 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 36 Sayı: 4

Kaynak Göster

APA Ersöz Kaya, İ., & Korkmaz, O. (2021). Machine Learning Approach for Predicting Employee Attrition and Factors Leading to Attrition. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(4), 913-928. https://doi.org/10.21605/cukurovaumfd.1040487