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Classification of Imbalanced Data Using Decision Tree and Bayesian Classifier

  • Ajay Malik
  • Abhishek Singh
  • Maroti DeshmukhEmail author
Conference paper
  • 165 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

The Bayesian classification is a method based on the Bayes theorem which gives best result when attributes are independent of each other and data is normalized. In this paper, a two-step approach is proposed to classify the data attributes which are locally normalized but not globally. The first step involves finding value for each attribute where the gain ratio is maximum. The classification occurs in the second step on the two separate parts of data using the Bayesian classifier. The experimental results show that the accuracy of the proposed method is better than the Bayesian classification and Decision tree.

Keywords

Gain ratio Decision tree Bayesian classification Imbalanced data 

References

  1. 1.
    Jahromi, A.H., Taheri, M.: A non-parametric mixture of Gaussian Naive Bayes classifiers based on local independent features. In: 2017 Artificial Intelligence and Signal Processing Conference (AISP), pp. 209–212 (2017)Google Scholar
  2. 2.
    Wei, W., Li, J., Cao, L., Ou, Y., Chen, J.: Effective detection of sophisticated online banking fraid on ectremely imbalanced data. World Wide Web 16, 449–475 (2013).  https://doi.org/10.1007/s11280-012-0178-0CrossRefGoogle Scholar
  3. 3.
    Au, T., Chin, M.-L., Ma, G.: Mining rare events data by sampling and boosting: a case study. In: Prasad, S., Vin, H., Sahni, S., Jaiswal, M., Thipakorn, B. (eds.) Information Systems, Technology and Management, vol. 54, pp. 373–379. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsletter 6(1), 20–29 (2004)CrossRefGoogle Scholar
  5. 5.
    Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting computational learning theory. In: Vitányi, P. (ed.) Computational Learning Theory, pp. 23–37. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  6. 6.
    Cardie, C.: Using decision trees to improve case-based learning. In: Proceedings of the 10th International Conference on Machine Learning, pp. 25–32. Morgan Kaufmann (1993)Google Scholar
  7. 7.
    Ratanamahatana, C.A., Gunopulos, D.: Feature selection for the naive Bayesian classifier using decision trees. Appl. Artif. Intell. 17(5–6), 475–487 (2003)CrossRefGoogle Scholar
  8. 8.
    Zhang, H., Sheng, S.: Learning weighted naive Bayes with accurate ranking. In: Proceedings of the 4th IEEE International Conference on Data Mining, pp. 567–570 (2004) Google Scholar
  9. 9.
    Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.M., Zietz, M., Hoffman, M.M., Xie, W., Rosen, G.L., Lengerich, B.J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A.E., Shrikumar, A., Xu, J., Cofer, E.M., Lavender, C.A., Turaga, S.C., Alexandari, A.M., Lu, Z., Harris, D.J., DeCaprio, D., Qi, Y., Kundaje, A., Peng, Y., Wiley, L.K., Segler, M.H.S., Boca, S.M., Swamidass, S.J., Huang, A., Gitter, A., Greene, C.S.: Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018)CrossRefGoogle Scholar
  10. 10.
    Ratanamahatana, C., Gunopulos, D.: Feature Selection for the naive Bayesian classifier using decision trees. Appl. Artif. Intell. 17, 475–487 (2003)CrossRefGoogle Scholar
  11. 11.
    Tsymbal, A., Puuronen, S., Patterson, D.: Feature selection for ensembles of simple Bayesian classifiers. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS (LNAI), vol. 2366, pp. 592–600. Springer, Heidelberg (2002)Google Scholar
  12. 12.
    Witten, F.E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo (2000)Google Scholar
  13. 13.
    Zheng, Z., Webb, G.I.: Lazy learning of Bayesian rules. Mach. Learn. 41, 53–84 (2000)CrossRefGoogle Scholar
  14. 14.
    Wang, B., Spencer, B., Ling, CX., Zhang, H.: Semi-supervised self-training for sentence subjectivity classification. In: Proceedings of 21st Conference on Advances in Artificial Intelligence, pp. 344–355 (2008)Google Scholar
  15. 15.
    Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models. In: WACV/MOTION, pp. 29–36 (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology, UttarakhandSrinagarIndia

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