An Empirical Evaluation of various Discrimination Measures for Discrimination Prevention

  • Authors

    • Mr. Manoj Ashok Wakchaure
    • Prof . Dr.S.S.Sane
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.28280
  • Data Quality, Discrimination Measures, Discrimination Prevention, Direct and Indirect Discrimination Prevention.

  • Abstract

    Discrimination prevention in Data mining has been studied by researchers. Several methods have been devised to take care of both direct and indirect discrimination prevention. In order to prevent discrimination, each of these methods tries to minimize the impact of discriminating attributes by modifying certain discriminating rules. The discriminating rules are identified using certain threshold and discrimination measure such as elift for direct discrimination and elb for indirect discrimination. Performance of these methods are measured and compared in terms discrimination removal using DDPD, DDPP for direct discrimination and IDPD, IDPP for indirect discrimination as well as resultant data quality using MC and GC for both kinds of discrimination.

    This paper deals with study of use of discrimination measures other than elift such as slift, clift and olift. The empirical evaluation presented here shows that slift provides best overall performance.

     

     

  • References

    1. [1] Sara Hajian and Josep Domingo-Ferrer A Methodology for Direct and Indirect Discrimination Prevention in Data Mining, Data Mining and Knowledge Discovery, vol. 25, no. 7, pp. 1445-1459, 2013

      [2] R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proc. 20th Intl Conf. Very Large Data Bases, pp. 487-499, 1994.

      [3] V. Verykios and A. Gkoulalas-Divanis, A Survey of Association Rule Hiding Methods for Privacy, PrivacyPreserving Data Mining, Models and Algorithms, C.C.Aggarwal and P.S. Yu, Springer, 2008.

      [4] D. Pedreschi, S. Ruggieri, and F. Turini, Measuring Discrimination in Socially-Sensitive Decision Records, Proc. Ninth SIAM Data Mining Conf. (SDM 09), pp. 581-592, 2009.

      [5] F. Kamiran and T. Calders, Classification without Discrimination, Proc. IEEE Second Intl Conf. Computer, Control and Comm.(IC4 09), 2009.

      [6] T. Calders and S. Verwer, Three Naive Bayes Approaches for Discrimination-Free Classification, Data Mining and Knowledge Discovery, vol. 21, no. 2, pp. 277-292, 2010.

      [7] F. Kamiran and T. Calders, Classification with no Discrimination, by Preferential Sampling, Proc. 19th Machine Learning Conf. Belgium and The Netherlands,2010.

      [8] D. Pedreschi, S. Ruggieri, and F. Turini, “DiscriminationAware Data Mining,†Proc. 14th ACM Int’l Conf. Knowledge Discovery and Data Mining (KDD ’08), pp. 560-568, 2008.

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  • How to Cite

    Manoj Ashok Wakchaure, M., & Dr.S.S.Sane, P. . (2018). An Empirical Evaluation of various Discrimination Measures for Discrimination Prevention. International Journal of Engineering & Technology, 7(4.19), 1025-1029. https://doi.org/10.14419/ijet.v7i4.19.28280

    Received date: 2019-03-10

    Accepted date: 2019-03-10

    Published date: 2018-11-27