skip to main content
10.1145/3451471.3451487acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicsimConference Proceedingsconference-collections
research-article

A Study on Performance and Accuracy Analysis of Classification Methods on Death of Beneficiaries in Social Security Context

Published: 13 July 2021 Publication History

Abstract

Several classification methods are experimentally analyzed in the context of social security fraud detection related to death of beneficiaries. The methods of Nearest Neighbor Classifier, Decision Tree and Bayesian Network Classifier are compared. The comparison is based on time taken to build the model to measure the performance and percentage correctly classified to measure the accuracy. This study is based on a data set of 9,683 death beneficiaries with Waikato Environment for Knowledge Analysis (WEKA) as data analysis tool. Subsequently, the method with better performance and accuracy will be used in our future analysis to discover other scenarios in social security fraud.

References

[1]
S. L. Weiss, and C. Kulikowski, “Computer Systems That Learn: Classification and Prediction Methods from Statistics,” Neural Networks, Machine Learning, and Expert Systems. San Francisco, Calif.: Morgan Kaufmann, 1991.
[2]
D. J. Hand, “Discrimination and Classification,” Chichester, U.K.: Wiley, 1981.
[3]
N. Bhatia, and Vandana, "Survey of Nearest Neighbor Techniques," International Journal of Computer Science and Information Security, Vol. 8, No. 2, pp. 302-305, 2010
[4]
T. M. Cover, and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Trans. Inform. Theory, Vol. IT-13, pp 21-27, Jan 1967.
[5]
T. Bailey, and A. K. Jain,” A note on Distance weighted k-nearest neighbor rules,” IEEE Trans. Systems, Man Cybernatics, Vol.8, pp 311-313, 1978.
[6]
K. Chidananda, and G. Krishna, “The condensed nearest neighbor rule using the concept of mutual nearest neighbor,” IEEE Trans. Information Theory, Vol IT- 25 pp. 488-490, 1979.
[7]
F Angiulli, “Fast Condensed Nearest Neighbor,” ACM International Conference Proceedings, Vol 119, pp 25-32.
[8]
K. Q. Weinberger, J. Blitzer, and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” NIPS, 2005.
[9]
T.A. McGlynn, A.A Suchkov, and E.L Winter, “Automated classification of ROSAT sources using heterogeneous multiwavelength source catalogs, “ ApJ, pp 616–1284, 2004.
[10]
Y. Zhang, and Y. Zhao, “A comparison of BBN, ADTree and MLP in separating Quasars from large survey catalogues,” ChJAA 7, pp 289–296, 2007.
[11]
J. S. R Jang, “ANFIS Adaptive Network Based Fuzzy inference System,” IEEE Transaction on Systems, Man and Cybernetics. Vol. 23, no3, pp 665-685, 1993
[12]
Y. Zhao and Y. Zhang, "Comparison of decision tree methods for finding active objects," Advances in Space Research, vol. 41, pp. 1955-1959, 2008.
[13]
R. Kohavi, “Scaling up the accuracy of naive-Bayes classifiers: a decisiontree hybrid,” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), AAAI Press, pp. 202–207, 1996.
[14]
I.H Witten, and E. Frank, “Data Mining: Practical Machine Learning Tools and Techniques,”, 2nd ed. Morgan Kaufmann, San Fransico, CA, 2005
[15]
L. Breiman, “Random forests,” Machine Learning. 45 (1), 5–32, 2001.
[16]
Y. Freund, and L. Mason, “The Alternating Decision Tree Learning Algorithm,” Proc. 16th Int',l Conf. Machine Learning (ICML ',99), pp.,124-133, 1999.
[17]
T.S. Lim, W.Y. Loh, and Y.S. Shih, “A comparison of prediction accuracy, complexity, and training time of thirty-tree old and new classification algorithms,” Machine Learning, Vol. 40, No. 3, pp. 203-228, 2000.
[18]
C. Rich, K. Nikos, and Y. Ainur, “An empirical evaluation of supervised learning in high dimensions,” Proceedings of the 25th international conference on Machine learning, p.96-103, 2008
[19]
J. Pearl, “Probabilistic Reasoning in Intelligent Systems,” 1988 :Kaufmann
[20]
J Cheng, and R Greiner, "Learning Bayesian Belief Network Classifiers: Algorithms and System,” Canadian Conference on AI, 2001
[21]
J. Cheng and R. Greiner, “Comparing Bayesian Network Classifiers,” Proc. Uncertainty in Artificial Intelligence (UAI), pp. 101-108, 1999.
[22]
G. Holmes, A. Donkin, and I.H. Witten. Weka: A machine learning workbench. In Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems, 1994.
[23]
G. Piatetsky-Shapiro, U. Fayyad, and P. Smith, "From Data Mining to Knowledge Discovery: An Overview," Advances in Knowledge Discovery and Data Mining, pp. 1-35. AAAI/MIT Press, 1996.
[24]
Attribute-Relation File Format, http://weka.wikispaces.com/ARFF

Index Terms

  1. A Study on Performance and Accuracy Analysis of Classification Methods on Death of Beneficiaries in Social Security Context
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICSIM '21: Proceedings of the 2021 4th International Conference on Software Engineering and Information Management
      January 2021
      251 pages
      ISBN:9781450388955
      DOI:10.1145/3451471
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 13 July 2021

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. WEKA
      2. classification methods
      3. data mining
      4. social security

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      ICSIM 2021

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 23
        Total Downloads
      • Downloads (Last 12 months)2
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 16 Feb 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media