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Rough Fuzzy Classification for Class Imbalanced Data

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Proceedings of Fourth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 335))

Abstract

This paper presents a new rough fuzzy classification approach for class imbalanced data. Here, interval type-2 fuzzy granulation of input features is formulated, various combinations of rough set extension-based methods are used to perform class imbalance learning, and K-nearest neighbor (KNN) classifier is used for data classification. The experimental results on the UCI data sets are reported to demonstrate the effectiveness of the proposed rough fuzzy classification model. Performance evaluation measures viz F-measure and geometric mean (G-mean) are used for analyzing classifier’s performance and suitability of the developed model for class imbalance learning.

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References

  1. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  2. García, V., Mollineda, R., Sánchez, J.: On the KNN performance in a challenging scenario of imbalance and overlapping. Pattern Anal. App. 11, 269–280 (2008)

    Article  Google Scholar 

  3. Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Data Mining and Knowledge Discovery Handbook, pp. 875–886 (2010)

    Google Scholar 

  4. Weiss, G.: Mining with rarity: a unifying framework. SIGKDD Explor. 6(1), 7–19 (2004)

    Article  Google Scholar 

  5. Drummond, C., Holte, R.C.: C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Chawla , N. et al. (eds.) Proceedings of ICML’03, Washington DC, USA (2003)

    Google Scholar 

  6. Pal, S.K., Skowron, A.: Rough-Fuzzy Hybridization: A New Trend in Decision Making. Springer, Singapore (1999)

    MATH  Google Scholar 

  7. Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. Neural Netw. 3, 683–697 (1992)

    Article  Google Scholar 

  8. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  9. Lin, T.Y.: Granulation and nearest neighborhoods: rough set approach. In: Pedrycz, W. (ed.) Granular Computing: An Emerging Paradigm, pp. 125–142. Physica-Verlag, Heidelberg (2001)

    Chapter  Google Scholar 

  10. Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 178, 3577–3594 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  11. Mazumder, R.U., Begum, S.A., Biswas, D.: An Exponential kernel based fuzzy rough sets model for feature selection. Int. J. Comput. Appl. (IJCA) 81(6), 24–31 (2013)

    Google Scholar 

  12. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    Book  MATH  Google Scholar 

  13. Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(1), 28–40 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  14. Genton, M.: Classes of kernels for machine learning: a statistics perspective. J. Mach. Learn. Res. 22, 99–312 (2001)

    Google Scholar 

  15. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Syst. 90, 111–127 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  16. Liang, Q., Mendel, J.M.: Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)

    Article  Google Scholar 

  17. Blake, C., Merz, C., Hettich, S., Newman, D.J.: UCI repository of machine learning databases. University of California, School of Information and Computer Sciences, Irvine, CA (1998)

    Google Scholar 

  18. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority oversampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    MATH  Google Scholar 

  19. Gu, Q., Zhu, L., Cai, Z.: Evaluation measures of the classification performance of imbalanced data sets. In: Cai, Z. et al. (eds.) Computational Intelligence and Intelligent Systems. Commun. Comput. Inf. Sci. 51, 461–471 (2009)

    Google Scholar 

  20. Buckland, M., Gey, F.: The Relationship between recall and precision. J. Am. Soc. Inf. Sci. 45(1), 12–19 (1994)

    Article  Google Scholar 

  21. Kubat, M., Holte, R.C., Matwin, S.: Machine learning for the detection of oil spills in satellite radar images. Mach. Learn. 30, 195–215 (1998)

    Article  Google Scholar 

  22. Liu, J., Hu, Q., Yu, D.: A weighted rough set based method developed for class imbalance learning. Inf. Sci. 178, 1235–1256 (2008)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgment

Author (RUM) gratefully acknowledges UGC for granting Maulana Azad National Fellowship for the research work.

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Authors and Affiliations

  1. Department of Mathematics, Assam University, Silchar, India

    Riaj Uddin Mazumder & Devajyoti Biswas

  2. Department of Computer Science, Assam University, Silchar, India

    Shahin Ara Begum

Authors
  1. Riaj Uddin Mazumder
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  2. Shahin Ara Begum
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  3. Devajyoti Biswas
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Corresponding author

Correspondence to Shahin Ara Begum .

Editor information

Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

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Mazumder, R.U., Begum, S.A., Biswas, D. (2015). Rough Fuzzy Classification for Class Imbalanced Data. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_14

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  • DOI: https://doi.org/10.1007/978-81-322-2217-0_14

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2216-3

  • Online ISBN: 978-81-322-2217-0

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