ABSTRACT
In economics research survey, the causal relationship between independent and dependent attributes has been frequently investigated by using regression linear models. However, not easy to achieve the high R-square value between both attributes if there are too many unclassified elements in data sets. This paper presents removing unclassified elements in conventional regression model using rough sets approximation. The proposed model is address to handle the unclassified academic staffs in data set which less contribution for supporting financial wellbeing decision. The result showed that number of unclassified staff has a positive effect to increase coefficient determination (R-square) value in the regression model. In this case study, the financial wellbeing of academic staff is significantly influenced by two different attributes, namely, financial behavior and financial stress. It also may help decision makers or universities management in improving their staff in financial wellness and wellbeing.
- Pawlak Z.: Rough sets. Int. J. Compt. Inf. Science. 11, 341--356 (1982)Google ScholarCross Ref
- Tay, F.E.H., Shen, L.: Economic and financial using rough sets model. European J. Operation Research. 141, 641--659 (2002)Google ScholarCross Ref
- Polkowski, L., Skowron, A.: Rough Sets in Knowledge Discovery 1: Methodology and Applications; Rough Sets in Knowledge Discovery 2: Application, Case Studies, and Sofware Systems. Physica-Verlag, Wurzburg (1998) Google ScholarDigital Library
- Shen, L., Tay, F.E.H., Qu, L., Shen, Y.: Fault Diagnosis using Rough Sets Theory. Computers in Industry (43), 61--72 (2000) Google ScholarDigital Library
- Krusinska, E., Slowinski, R., Stefanowski, J.: Discriminant versus Rough Set Approach to Vague Data Analysis. Applied Stochastic Models and Data Analysis (8), 43--56 (1992)Google Scholar
- Efendi, R., Deris, M. M.: Decision Support Model in Determining Factors and Its Dominant Criteria Affecting Cholesterol Level Based on Rough-Regression. Recent Advances on Soft Computing and Data Mining, 243--251 (2018)Google Scholar
- Efendi, R., Samsudin, N.A., Deris, M.M.: Medipre: Medical Diagnosis Prediction using Rough-Regression Approximation. ACM Proceeding on High Compilation, Computing and Communications, 35--39 (2018) Google ScholarDigital Library
- Efendi, R., Samsudin, N. A., Deris, M. M, and Ting Y. G: Flu Diagnosis System Using Jaccard Index and Rough Sets Approaches. Journal of Physic, Conference Series (2018)Google Scholar
- Rasyidah, Nawi, N. M and Efendi, R: Rough-Regression Model for Investigating Product Attributes and Purchase Decision. IEEE Xplore Proceeding on Computer and Communication Engineering, (2018).Google ScholarCross Ref
- Herawan, T., Deris, M. M., Abawajy, H.: A Rough Sets Approach for Selecting Clustering Attribute. Knowledge-Based Systems. 23, 220--231 (2010) Google ScholarDigital Library
- Wooldridge, M.: Introductory econometrics a modern approach. Third Ed. Thomson, South Western, USA (2006)Google Scholar
- Chapman, P., Clinton, J., Khabaza, T., Reinartz, T and Wirth, R. The CRISP-DM Process Model. August (2000)Google Scholar
- Rissino, S., Torres, G. L. Rough set theory-fundamental concepts, principals, data extraction, and applications, Julio Ponce and Adam Karahoca (Ed), Data Mining and Knowledge Discovery in Real Life App. Inform (2009) 35--58.Google Scholar
Index Terms
- Removing Unclassified Elements in Investigating of Financial Wellbeing Attributes Using Rough-Regression Model
Recommendations
A Research on Transnational Financial Stress Spillover Based on Time-Frequency Perspective
AbstractUsing BK's frequency connectivity method, this paper studies the static and dynamic volatility spillovers between regional financial markets at different frequencies from 2006 to 2022, and during the Global Financial Crisis, the European Debt ...
Use of Technology and SME Managers' Financial Literacy in Developing Economies
ICEBT '18: Proceedings of the 2018 2nd International Conference on E-Education, E-Business and E-TechnologyOur study aims at analyzing the perceived impact of technology utilization by Micro, small and medium enterprises (SMEs) on managers' financial literacy in developing economy setting. Employing a survey sample of 311 SMEs from Tanzania we use the ...
Comments