ABSTRACT
The objective of this study was to automate job performance prediction based on DISC personality test. We transformed this problem to Multi-Label Classification (MLC) by using employee's job performances as labels. In this study, three widely used MLC techniques have been employed such as Binary Relevance (BR), Label Powerset (LP) and Classifier Chains (CC) for prediction of job performances. However, these traditional techniques didn't show promising results. Therefore, we proposed another approach by building stacking MLC with model selection. The proposed method has three steps: (1) building MLC model; (2) using process from the first step and applying with a stacking model and (3) utilizing feature selection technique to select the proper models for final prediction. Using the surveys from a big financial company in Thailand, we found that the last proposed approach shows better performance, compared to the traditional MLC.
- Cascio, F. W. and Aguinis, H. 2008. Research in Industrial and Organizational Psychology From 1963 to 2007: Changes, Choices, and Trends. in Journal of Applied Psychology 2008, Vol. 93, No. 5, 1062--1081Google Scholar
- Spitzmuller, M., Dyne, L. V. and Ilie, R. 2008. Organizational Citizenship Behavior. A Review and Extension of its Nomological NetworkGoogle Scholar
- Waheed, A., Yang, J. and Webber, J. 2018. THE EFFECT OF PERSONALITY TRAITS ON SALES PERFORMANCE: AN EMPIRICAL INVESTIGATION TO TEST THE FIVE-FACTOR MODEL (FFM) IN PAKISTAN. in Interdisciplinary Journal of Information, Knowledge, and Management, Vol. 12Google Scholar
- Weiming, G. 2011. Study on the Application of DISC Behavioral Style in Talent Management in Banking Industry. Proceedings of the 8th International Conference on Innovation & ManagementGoogle Scholar
- Yong, K. Y., Hwa, B. Y., Hyun, P. H., Hyang, Y. J., and Su, J. E. 2012. The Effects of DISC Behavior Styles of Office Workers on Job Satisfaction, Organizational Commitment and Job Performance. Korean J Occup Health Nurs. 2012 Aug;21(2):98--107Google Scholar
- Tabasum, F., Ibrahim, M. Rabbani, M. and Asif, M. 2014. Impact of Salesmen Personality on Customer Perception and Sales. Global Journal of Management and Business Research: E MarketingGoogle Scholar
- Agodi, J. E., Ahaiwe, E. O. and Awah, A. E. 2017. Salesman's Personality Trait and Its Effect on Sales Performance: Study of Fast Moving Consumer Goods (FMCG) in Abia State, Nigeria. in Journal of Economics and Sustainable Development, Vol.8, No.24Google Scholar
- Yata, A., Kante, P., Sravani, T. and Malathi, B. 2018. Personality Recognition using Multi-Label Classification. International Research Journal of Engineering and Technology (IRJET), Volume: 05 Issue: 03Google Scholar
- Doquire, G. and Verleysen, M. 2011. Feature Selection for Multi-label Classification Problems. Université catholique de LouvainGoogle Scholar
- Kafrawy, P. E., Mausad, A. and Esmail, H. 2015. Experimental Comparison of Methods for Multi-Label Classification in Different Application Domains. in International Journal of Computer Applications, Volume 114Google Scholar
- Santos, A. M., Canuto, A. M. P. and Neto, A. F. 2011. A Comparative Analysis of Classification Methods to Multi-label Tasks in Different Application Domains. in International Journal of Computer Information Systems and Industrial Management Applications, Volume 3Google Scholar
- Ganda, D. and Buch, R. 2018. A Survey on Multi Label Classification. in Recent Trends in Programming Languages, Volume 5, Issue 1Google Scholar
- Menahem, E., Rokach, L. and Elovici. Y. 2009. An improved stacking schema for classification tasks. Department of Information Systems Engineering, Ben-Gurion University and Deutsche Telekom Laboratories at Ben-Gurion University, Be'er Sheva 84105, IsraelGoogle Scholar
- Wolpert, D. H. 1992. Stacked generalization. Neural networks, Volume 5, Issue 2, Pages 241--259Google Scholar
- Spolaôr, N., Cherman, E. A., Monard, M. C. and Lee, H. D. 2013. A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach. in Electronic Notes in Theoretical Computer Science 292:135--151Google ScholarDigital Library
- Nanak, C., Preeti, M., C., Rama, K., Emmanuel, S. P. and Mahesh, C. G. 2016. A Comparative Analysis of SVM and its Stacking with other Classification Algorithm for Intrusion Detection. International Conference on Advances in Computing, Communication, & Automation (ICACCA)Google Scholar
- Nazlia, O., Mohammed, A., Adel, Q. A., Tareq, A. 2013. Ensemble of Classification Algorithms for Subjectivity and Sentiment Analysis of Arabic Customers' Reviews. in International Journal of Advancements in Computing TechnologyGoogle Scholar
- Erik, M. S., Douglas, T. and Youngmoo, E. K. 2010. Feature Selection for Content-Based, Time-Varying Musical Emotion Regression. Published in Multimedia Information Retrieval.Google Scholar
- Asim, M. N., Rehman, A. and Shoaib, U. 2017. Accuracy Based Feature Ranking Metric for Multi-Label Text Classification. in International Journal of Advanced Computer Science and Applications, Vol. 8, No. 10Google Scholar
- Santos, A. M., Canuto, A. M. P. and Neto, A. F. 2011. A Comparative Analysis of Classification Methods to Multi-label Tasks in Different Application Domains. in International Journal of Computer Information Systems and Industrial Management Applications, Vol. 3, pp. 218--227Google Scholar
Index Terms
- Multi-Label Classification of Employee Job Performance Prediction by DISC Personality
Recommendations
Incorporating label dependency into the binary relevance framework for multi-label classification
In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label ...
Interdependence Model for Multi-label Classification
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time SeriesAbstractThe multi-label classification problem is a supervised learning problem that aims to predict multiple labels for each data instance. One of the key issues in designing multi-label learning approaches is how to incorporate dependencies among ...
Dynamic ensemble learning for multi-label classification
AbstractEnsemble learning has been shown to be an effective approach to solve multi-label classification problem. However, most existing ensemble learning methods do not consider the difference between unseen instances, and existing methods that consider ...
Comments