Personnel selection and prediction of organizational positions using data mining algorithms (case study: Mammut industrial complex)

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.

2 Department of Industrial Engineering, Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran.

10.22105/jarie.2021.233010.1170

Abstract

This study aims to identify and employ qualified individuals and assign different organizational positions. Accordingly, a data mining approach is proposed. This paper presents an empirical study which has important practical application in modern human resource management. Therefore, effective features on staff selection are extracted from literature and entered into the database after expert approval respectively. Further, the impact of each feature on staff selection is determined and the ability of applied classification algorithms is compared. The results represent that the organizational position feature has a great impact on forecasting of selection or rejection. Data mining algorithms used in this study have acceptable performance based on accuracy rate, and J48 algorithm performs better comparing to other algorithms based on accuracy rate, recall, F-measure and area under Receiver Operating Characteristic (ROC) curve. Three features of background, level of education, and major are identified as effective features in association rules. Finally, an approach is presented for applying data mining algorithms in employees hiring and organizational positions assignment procedure

Keywords

Main Subjects


Fatemi, M., & Karbasian, M. (2015). Performance assessment in Isfahan municipality via knowledge management and organizational agility approach using data envelopment analysis. Journal of applied research on industrial engineering, 2 (3), 154-167.
[2] Lievens, F. (2002). Recent trends and challenges in personnel selection. Personnel review, 31(5-6), 580-601.
[3] Ajripour, I., Asadpour, M., & Tabatabaie, L. (2019). A model for organization performance management applying mcdm and bsc: a case study. journal of Applied Research on Industrial Engineering, 6(1), 52-70.
[4] Borman, W. C., Hanson, M. A., & Hedge, J. W. (1997). Personnel selection. Annual review of psychology, 48, 299-337.
[5] Robertson, I. T., & Smith, M. (2001). Personnel Selection. Journal of Occupational and Organizational Psychology, 74(4), 441-472.
[6] Chien, C. F., & Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry. Expert systems with applications, 34(1), 280-290.
[7] Santos, A. D., Armanu, A., Setiawan, M., & Rofiq, A. (2020). Effect of recruitment, selection and culture of organizations on state personnel performance. Management science letters, 10, 1179-1186.
[8] Shaeiri, Z., & Ghaderi, R. (2012). Modification of the fast global k-means using a fuzzy relation with application in microarray data analysis. International journal of engineering-transactions C: aspects, 25(4), 283-292.
[9] Darvishi, A., & Hassanpour, H. (2015). A geometric view of similarity measures in data mining. International journal of engineering-transactions C: aspects, 28(12), 1728-1737.
[10] Sharma, A. K., Lakhtaria, K., & Vishwakarma, S. (2013). Data mining based predictions for employees skill enhancement using pro-skill-improvement program and performance using classifier scheme algorithm. International journal of advanced research in computer science, 4(3), 102-107.
[11] Esmaieeli, A. M., Ghousi, R. E., & Esmaieeli, A. (2015). A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing). Journal of industrial and systems engineering, 8(4), 106-121.
[12] Mohammad Naser, M., Shaaban, E., & Samir, A. (2019). A proposed model for predicting employees' performance using data mining techniques: egyptian case study. International journal of computer science and information security, 17(1), 31-40.
[13] Dursun, M., & Karsak, E. E. (2010). Expert systems with applications a fuzzy MCDM Approach for personnel selection. Expert systems with applications, 37(6), 4324-4330.
[14] Verma, M., & Rajasankar, J. (2017). A Thermodynamical approach towards group multi-criteria decision making (GMCDM) and its application to human resource Selection. Applied soft computing journal, 52, 232-332.
[15] Jantan, H., Hamdan, A. R., Othman, Z. A., & Puteh, M. (2010, May). Applying data mining classification techniques for employee's performance prediction. Knowledge management 5th international conference (KMICe2010) (pp. 645-652).
[16] Chen, L. F., & Chien, C. F. (2011). Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries. Flexible services and manufacturing journal, 23, 263-289.
[17] Strohmeier, S., & Piazza, F. (2013). expert systems with applications domain driven data mining in human resource management : a review of current research. Expert systems with applications, 40(7), 2410-2420.
[18] Gupta, A., & Garg, D. (2014). Applying data mining techniques in job recommender system for considering candidate job preferences. International conference on advances in computing, communications and informatics (ICACCI) (pp. 1458-1465). New Delhi, India.
[19] Sharma, M., & Goyal, A. (2015). An application of data mining to improve personnel performance evaluation in higher education sector in India. International conference on advances in computer engineering and applications (pp. 559-564). Ghaziabad.
[20] Sebt, M. V., & Yousefi, H. (2015). Comparing data mining approach and regression method in determining factors affecting the selection of human resources comparing data mining approach and regression method in determining factors affecting the selection of human resources. Cumhuriyet science journal, 36(4), 1846-1859.
[21] Mishra, T. (2016). Students ’ employability prediction model through data mining. International journal of applied engineering research, 11(4), 2275-2282.
[22] Kirimi, J. M., & Moturi, C. A. (2016). Application of data mining classification in employee performance prediction. International journal of computer applications, 146(7), 28-35.
[23] Kamatkar, S. J., Tayade, A., Viloria, A., & Hernandez-Chacin, A. (2018). Application of classification technique of data mining for employee management system. Data mining and big data (pp. 434-444).
[24] Borkar, S., & Rajeswari, K. (2013). Predicting students academic performance using education data mining. International journal of computer science and mobile computing, 2(7), 273-279.
[25] Kaur, P., Singh, M., & Josan, G. S. (2015). Classification and prediction based data mining algorithms to predict slow learners in education sector. 3rd international conference on recent trends in computing 2015(ICRTC-2015) (pp. 500-508). Procedia Computer Science.
[26] Chang, C. L. (2007). A study of applying data mining to early intervention for developmentally-delayed children. Expert systems with applications, 33(2), 407–412.
[27] Mohammadi, M., Iranmanesh, S. H., Tavakoli-Moghaddam, R., & Abdollahzadeh, M. (2014). hierarchical alpha-cut fuzzy C-means, fuzzy ARTMAP and cox regression model for customer churn prediction. International journal of engineering- TRANSACTIONS C: aspects, 27(9), 1405-1414.
Sut, N., & Simsek, O. (2011). Comparison of regression tree data mining methods for prediction of mortality in head injury. Expert systems with applications38(12), 15534-15539.
[29] Huang, C. T., Lin, W. T., Wnag, S. T., & Wang, W. S. (2009). Planning of educational training courses by data mining: using China motor corporation as an example. Expert systems with applications, 36(3), 7199–7209.
[30] Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in society, 24(4), 483-502.
[31] Kotsiantis, S. B. (2007). Supervised machine learning : a review of classification techniques. Informatica, 31, 249-268.
[32] Domingos, P., & Pazzani, M. (1997). On the optimality of the simple bayesian classifier under zero-one loss. Machine learning, 29, 103-130.
[33] Abu Saa, A. (2016). Educational data mining & students’ performance prediction. international journal of advanced computer science and applications, 7(5), 212-220.
[34] Movahedi Sobhani, F., & Madadi, T. (2015). Studying the suitability of different data mining methods for delay analysis in construction projects. Journal of applied research on industrial engineering, 2(1), 15-33.
[35] Bhuvaneswari, T., Prabaharan, S., & Subramaniyaswamy, V. (2015). An rffrctive prediction analysis using J48. ARPN journal of engineering and applied sciences, 10(8), 3474-3480.
[36] Panigrahi, R., & Borah, S. (2018). Rank allocation to J48 group of decision tree classifiers using binary and multiclass intrusion detection datasets. International conference on computational intelligence and data science (ICCIDS 2018) (pp. 323-332). The NorthCap University, India.
[37] Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD conference (pp. 207-216). Washington, USA.
[38] Hashemzadeh, E., & Hamidi, H. (2016). Using a data mining tool and FP-growth algorithm application for extraction of the rules in two different dataset. International journal of engineering- TRANSACTIONS C: aspects, 29(6), 788-796.
[39] Biglari, M., Mirzaei, F., & Hassanpour, H. (2020). Feature selection for small sample sets with high dimensional data using heuristic hybrid approach. International journal of engineering- TRANSACTIONS B: applications, 33(2), 213-220.
[40] F Bagherzadeh, F., Ramezankhani, A., Azizi, F., Hadaegh, F., & Khalili, D. (2016). A tutorial on variable selection for clinical prediction models: Feature selection methods in data-mining could improve the results. Journal of clinical epidemiology, 71, 76-85.
[41] Diamantidis, N. A., Karlis, D., & Giakoumakis, E. A. (2000). Unsupervised Stratification of Cross-Validation for Accuracy Estimation. Artificial Intelligence, 116(1-2), 1-16.
[42] Alort, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79.
[43] Hamidi, H., & Daraei, A. (2016). Analysis of pre-processing and post-processing methods and using data mining to diagnose heart diseases. International journal of engineering- TRANSACTIONS A: basics, 29(7), 921-930.
[44] Han, J., & Kamber, M. (2006). Data mining: concepts and techniques (2nd edition). Morgan Kaufmann.
[45] Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in human behavior73, 247-256.
[46] Kotsiantis, S., & Kanellopoulos, D. (2006). Association rules mining : a recent overview basic concepts & basic association rules algorithms. International transactions on computer science and engineering, 32(1), 71-82.
[47] Mammutco. (2018). Mammut Industrial Group. Retrieved from https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwiIgrCWmZLoAhWCwqYKHYjJCqYQFjAAegQIGBAC&url=http%3A%2F%2Fwww.en.mammutco.com%2F&usg=AOvVaw1llIV-ffBKUdb_JoSyBL82."