Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3316615.3316651acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicscaConference Proceedingsconference-collections
research-article

Removing Unclassified Elements in Investigating of Financial Wellbeing Attributes Using Rough-Regression Model

Published: 19 February 2019 Publication History

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.

References

[1]
Pawlak Z.: Rough sets. Int. J. Compt. Inf. Science. 11, 341--356 (1982)
[2]
Tay, F.E.H., Shen, L.: Economic and financial using rough sets model. European J. Operation Research. 141, 641--659 (2002)
[3]
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)
[4]
Shen, L., Tay, F.E.H., Qu, L., Shen, Y.: Fault Diagnosis using Rough Sets Theory. Computers in Industry (43), 61--72 (2000)
[5]
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)
[6]
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)
[7]
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)
[8]
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)
[9]
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).
[10]
Herawan, T., Deris, M. M., Abawajy, H.: A Rough Sets Approach for Selecting Clustering Attribute. Knowledge-Based Systems. 23, 220--231 (2010)
[11]
Wooldridge, M.: Introductory econometrics a modern approach. Third Ed. Thomson, South Western, USA (2006)
[12]
Chapman, P., Clinton, J., Khabaza, T., Reinartz, T and Wirth, R. The CRISP-DM Process Model. August (2000)
[13]
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.

Cited By

View all
  • (2023)A Scientometric Analysis of Wellbeing Research in the Construction IndustrySustainability10.3390/su15241666215:24(16662)Online publication date: 8-Dec-2023

Index Terms

  1. Removing Unclassified Elements in Investigating of Financial Wellbeing Attributes Using Rough-Regression Model

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer Applications
      February 2019
      611 pages
      ISBN:9781450365734
      DOI:10.1145/3316615
      © 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

      In-Cooperation

      • University of New Brunswick: University of New Brunswick

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 19 February 2019

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Rough-regression
      2. financial behavior
      3. financial stress
      4. financial wellbeing
      5. unclassified element

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      ICSCA '19

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)5
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 20 Nov 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2023)A Scientometric Analysis of Wellbeing Research in the Construction IndustrySustainability10.3390/su15241666215:24(16662)Online publication date: 8-Dec-2023

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media