Advertisement

Investigating Determinants of Profitability of Commercial Firms: Rough Set Analysis

  • Arpit SinghEmail author
  • Subhas Chandra Misra
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1089)

Abstract

To achieve excellence in any business venture and have an edge over the competitors, it is necessary to optimally use the available scarce resources which form the foundation of a perpetually flourishing business enterprise. This paper employs rough set theory to categorically remove any superfluous data present in the system by establishing a discernibility matrix which accommodates the elements that differentiates the objects or the equivalence classes obtained using the indiscernibility relation. The basic principle, to achieve the objective of data reduction, is to minimize the Boolean expression obtained by logically concatenating entries of the discernibility matrix. The reduced information is subjected to the standard statistical regression procedures and is found that it is statistically consistent. Finally, an artificial neural network modeling is suggested which validates the results obtained using rough set analysis for the relations between the data variables or the given information other than the linear ones.

Keywords

Rough sets Discernibility matrix Vagueness Reducts 

References

  1. 1.
    Sreekumar, Panda, B.: Business intelligence: an overview. J. IPM, 5(2), 28–32 (2005)Google Scholar
  2. 2.
    Dubois, D., Prade, H., Pawlak, Z.: Foreword. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)Google Scholar
  3. 3.
    Grzymala-Busse, J.W.: Knowledge acquisition under uncertainty—A rough set approach. J. Intel. Rob. Syst. 1(1), 3–16 (1988). Grzymala-Busse, J.W.: Managing Uncertainty in Expert Systems. Kluwer, Dordrecht (1991)Google Scholar
  4. 4.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Advances and Applications of the Rough Set Theory, pp. 331–362. Kluwer, Dordrecht (1992)Google Scholar
  5. 5.
    Pawlak, Z.: Rough Sets. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Poland (1982)zbMATHGoogle Scholar
  6. 6.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Qian, J., Xia, M., Yue, X.: Parallel knowledge acquisition algorithms for big data using MapReduce. Int. J. Mach. Learn. Cybernet. 9(6), 1007–1021 (2018)CrossRefGoogle Scholar
  8. 8.
    Qian, Y., Liang, X., Wang, Q., Liang, J., Liu, B., Skowron, A., Dang, C.: Local rough set: a solution to rough data analysis in big data. Int. J. Approximate Reasoning 97, 38–63 (2018)Google Scholar
  9. 9.
    Durairaj, M., Meena K.: A hybrid prediction system using rough sets and artificial neural networks. Int. J. Innovative Technol. Creative Eng. 1(7) (2011). (ISSN: 2045–8711)Google Scholar
  10. 10.
    Abed-Elmdoust, A., Kerachian, R.: Wave height prediction using rough set theory, Ocean Eng. (2012)Google Scholar
  11. 11.
    Lashteh Neshaei, M.A., Pirouz, M.: Comp. Meth. Civil Eng. 1(1), 85–94 (2010)Google Scholar
  12. 12.
    Yang, Y., Chen, D., Wang, H.: Active sample selection based incremental algorithm for attribute reduction with rough sets. IEEE Trans. Fuzzy Syst. 25(4), 825–838 (2017)CrossRefGoogle Scholar
  13. 13.
    Dai, J., Hu, Q., Hu, H., Huang, D.: Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans. Fuzzy Syst. 26(2), 937–950 (2018)CrossRefGoogle Scholar
  14. 14.
    El Aziz, M.A., Hassanien, A.E.: Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput. Appl. 29(4), 925–934 (2018)CrossRefGoogle Scholar
  15. 15.
    Chen, Y., Zeng, Z., Lu, J.: Neighborhood rough set reduction with fish swarm algorithm. Soft Comput. 21(23), 6907–6918 (2017)CrossRefGoogle Scholar
  16. 16.
    Bose, I.: Deciding the financial health of dotcoms using rough sets. Inf. Manag. 43(7), 835–846 (2006)CrossRefGoogle Scholar
  17. 17.
    Falc, R.: Rough set theory: a true landmark in data analysis, vol. 174, Springer Science & Business Media (2009)Google Scholar
  18. 18.
    Ziarko, W. (ed.): Rough sets, fuzzy sets and knowledge discovery. In: Proceedings of RSKD’94 Workshop (Banff). Springer, Berlin (1994)Google Scholar
  19. 19.
    Wu, S.: An algorithm for clustering data based on rough set theory. In: International Symposium on Information Science and Engineering (2008)Google Scholar
  20. 20.
    Huang, C.C., Tseng, T.L., Jiang, F., Fan, Y.N., Hsu, C.H.: Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes. Ann. Oper. Res. (2014)Google Scholar
  21. 21.
    Pratiwi, L.: An empirical study of density and distribution functions for ant swarm optimized rough reducts. In: Communications in Computer and Information Science (2011)Google Scholar
  22. 22.
    Ziarko, W., Golan, R., Edwards, D.: An application of datalogic/R knowledge discovery tool to identify strong predictive rules in stock market data. In: Proceedings of AAAI Workshop on Knowledge Discovery in Databases, pp. 89–101. Washington, D.C (1993)Google Scholar
  23. 23.
    Hou, Zhijian, Lian, Zhiwei, Yao, Ye, Yuan, Xinjian: Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Appl. Energy 83, 1033–1046 (2006)CrossRefGoogle Scholar
  24. 24.
    Mahapatra, S., Sreekumar., Mahapatra, S.S.: Attribute selection in marketing: a rough set approach. Sci. Dir. IIMB Manage. Rev. 22, 16–24 (2010)Google Scholar
  25. 25.
    Hanke, J.E., Reitsch, A.G., Wichern D.W.: Business forecasting, vol.9, Upper Saddle River, NJ:Prentice Hall (2001)Google Scholar
  26. 26.
    Pawlak, Z., Slowinski, R.: Rough set approach to multi-attribute decision analysis. Eur. J. Oper. Res. (1994)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Industrial & Management EngineeringIITKanpurIndia

Personalised recommendations