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Exploring Neighborhood Structures with Neighborhood Rough Sets in Classification Learning

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Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 42))

Abstract

We introduce neighborhoods of samples to granulate the universe and use the neighborhood granules to approximate classification, thus they derived a model of neighborhood rough sets. Some machine learning algorithms, including boundary sample selection, feature selection and rule extraction, were developed based on the model.

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References

  1. Andersen, T.L., Martinez, T.R.: NP-completeness of minimum rule sets. In: Proceedings of the 10th International Symposium on Computer and Information Sciences, Kusadasi, Aydin, Turkey, pp. 411–418 (1995)

    Google Scholar 

  2. Cortes, C., Vapnik, V.N.: Support vector networks. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  3. Chen, D.G., Wang, C.Z., Hu, Q.H.: A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Information Sciences 177(17), 3500–3518 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Düntsch, I., Gediga, G.: Statistical evaluation of rough set dependency analysis. International Journal of Human Computer Studies 46(5), 589–604 (1997)

    Article  Google Scholar 

  5. Fierens, D., Ramon, J., Blockeel, H., Bruynooghe, M.: A comparison of pruning criteria for probability trees. Machine Learning 78, 251–285 (2010)

    Article  Google Scholar 

  6. Gilad-Bachrach, R., Navot, A., Tishby, N.: Margin based feature selection - theory and algorithms. In: Proc. 21st Int. Conf. Mach., pp. 43–50. ACM, New York (2004)

    Google Scholar 

  7. Hu, X., Cercone, N.: Learning in Relational Databases: A Rough Set Approach. Computational Intelligence 11(2), 323–338 (1995)

    Article  Google Scholar 

  8. Hu, Q.H., Pedrycz, W., Yu, D.R., Lang, J.: Selecting discrete and continuous features based on neighborhood decision error minimization. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 40(1), 137–150 (2010)

    Article  Google Scholar 

  9. Hu, Q.H., Yu, D.R., Liu, J.F., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Information Sciences 178(18), 3577–3594 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hu, Q.H., Yu, D.R., Xie, Z.X.: Neighborhood classifier. Expert Systems with Applications 34, 866–876 (2008)

    Article  Google Scholar 

  11. Hu, Q.H., Zhang, L., Zhang, D., et al.: Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Systems with Applications 38, 10737–10750 (2011)

    Article  Google Scholar 

  12. He, Q.H., Xie, Z., Hu, Q.H., et al.: Neighborhood based sample and feature selection for SVM classification learning. Neurocomputing 74, 1585–1594 (2011)

    Article  Google Scholar 

  13. Du, Y., Hu, Q.H., Zhu, P.F., et al.: Rule learning for classification based on neighborhood covering reduction. Information Sciences 181(24), 5457–5467 (2011)

    Article  MathSciNet  Google Scholar 

  14. Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: Rough and fuzzy-rough-based approaches. IEEE Transactions of Knowledge and Data Engineering 16(12), 1457–1471 (2004)

    Article  Google Scholar 

  15. Koggalage, R., Halgamuge, S.: Reducing the number of training samples for fast support vector machine classification. Neural Information Processing 2(3), 57–65 (2004)

    Google Scholar 

  16. Lee, Y.J., Mangasarian, O.L.: RSVM: Reduced support vector machines. Data Mining Institute Technical Report 00-07 (July 2000); First SIAM International Conference on Data Mining, Chicago (2001)

    Google Scholar 

  17. Moshkov, M.J., Skowron, A., Suraj, Z.: On minimal rule sets for almost all binary information systems. Fundamenta Informaticae 80, 247–258 (2008)

    MathSciNet  Google Scholar 

  18. Palawk, Z.: Rough sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)

    Article  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  20. Ślęzak, D.: Approximate entropy reducts. Fundamenta informaticae 53(3-4), 365–390 (2002)

    MathSciNet  Google Scholar 

  21. Shin, H., Cho, S.: Invariance of neighborhood relation under input space to feature space mapping. Pattern Recognition Letters 26, 707–718 (2005)

    Article  Google Scholar 

  22. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of Rough Set Theory, pp. 331–362. Kluwer, Dordrecht (1991)

    Google Scholar 

  23. Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24, 833–849 (2003)

    Article  MATH  Google Scholar 

  24. Tsumoto, S.: Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Systems with Applications 24(2), 189–197 (2003)

    Article  Google Scholar 

  25. Wang, J., Miao, D.Q.: Analysis on attribute reduction strategies of rough sets. Journal of Computer Science and Technology 13(2), 189–192 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  26. Wang, J., Wang, J.: Reduction algorithms based on discernibility matrix: The ordered attributes method. Journal of Computer Science and Technology 16(6), 489–504 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  27. Wang, X.Y., Yang, J., Teng, X.L., et al.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28(4), 459–471 (2007)

    Article  Google Scholar 

  28. Wang, G.Y., Zhao, J., An, J.J., et al.: A comparative study of algebra viewpoint and information viewpoint in attribute reduction. Fundamenta Informaticae 68(3), 289–301 (2005)

    MathSciNet  MATH  Google Scholar 

  29. Ziarko, W.: Variable precision rough sets model. Journal of Computer and System Sciences 46, 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhong, N., Dong, J., Ohsuga, S.: Rule discovery by soft induction techniques. Neurocomputing 36(1-4), 171–204

    Google Scholar 

  31. Zhao, M., Han, S.Q., Wang, J.: Tree expressions for information systems. Journal of Computer Science and Technology 22(2), 297–307 (2007)

    Article  MathSciNet  Google Scholar 

  32. Zhu, W., Wang, F.Y.: On three types of covering-based rough sets. IEEE Transactions on Knowledge and Data Engineering 19(8), 1131–1144 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. Harbin Institute of Technology, Box 458, Harbin, 150001, Heilongjiang Province, P.R. China

    Qinghua Hu, Leijun Li & Pengfei Zhu

Authors
  1. Qinghua Hu
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  2. Leijun Li
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  3. Pengfei Zhu
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Corresponding author

Correspondence to Qinghua Hu .

Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, Warsaw, 02097, Poland

    Andrzej Skowron

  2. Institute of Computer Science, University of Rzeszów, ul. Dekerta 2, Rzeszów, 35-030, Poland

    Zbigniew Suraj

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Hu, Q., Li, L., Zhu, P. (2013). Exploring Neighborhood Structures with Neighborhood Rough Sets in Classification Learning. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30344-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-30344-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30343-2

  • Online ISBN: 978-3-642-30344-9

  • eBook Packages: EngineeringEngineering (R0)

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