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Mining Incomplete Data with Many Lost and Attribute-Concept Values

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Rough Sets and Knowledge Technology (RSKT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9436))

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Abstract

This paper presents experimental results on twelve data sets with many missing attribute values, interpreted as lost values and attribute-concept values. Data mining was accomplished using three kinds of probabilistic approximations: singleton, subset and concept. We compared the best results, using all three kinds of probabilistic approximations, for six data sets with lost values and six data sets with attribute-concept values, where missing attribute values were located in the same places. For five pairs of data sets the error rate, evaluated by ten-fold cross validation, was significantly smaller for lost values than for attribute-concept values (5 % significance level). For the remaining pair of data sets both interpretations of missing attribute values do not differ significantly.

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References

  1. Clark, P.G., Grzymala-Busse, J.W.: Experiments on probabilistic approximations. In: Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 144–149 (2011)

    Google Scholar 

  2. Clark, P.G., Grzymala-Busse, J.W.: Rule induction using probabilistic approximations and data with missing attribute values. In: Proceedings of the 15-th IASTED International Conference on Artificial Intelligence and Soft Computing ASC 2012, pp. 235–242 (2012)

    Google Scholar 

  3. Clark, P.G., Grzymała-Busse, J.W.: An experimental comparison of three interpretations of missing attribute values using probabilistic approximations. In: Ciucci, D., Inuiguchi, M., Yao, Y., śȩzak, D. (eds.) RSFDGrC 2013. LNCS, vol. 8170, pp. 77–86. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Clark, P.G., Grzymala-Busse, J.W.: Mining incomplete data with lost values and attribute-concept values. In: Proceedings of the 2014 IEEE International Conference on Granular Computing, pp. 49–54 (2014)

    Google Scholar 

  5. Grzymala-Busse, J.W.: LERS–a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  6. Grzymala-Busse, J.W.: MLEM2: a new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 243–250 (2002)

    Google Scholar 

  7. Grzymała-Busse, J.W.: Generalized parameterized approximations. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 136–145. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  8. Grzymala-Busse, J.W., Wang, A.Y.: Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values. In: Proceedings of the 5-th International Workshop on Rough Sets and Soft Computing in conjunction with the Third Joint Conference on Information Sciences, pp. 69–72 (1997)

    Google Scholar 

  9. Grzymala-Busse, J.W., Ziarko, W.: Data mining based on rough sets. In: Wang, J. (ed.) Data Mining: Opportunities and Challenges, pp. 142–173. Idea Group Publ., Hershey (2003)

    Chapter  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  11. Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man-Mach. Stud. 29, 81–95 (1988)

    Article  Google Scholar 

  12. Ślȩzak, D., Ziarko, W.: The investigation of the bayesian rough set model. Int. J. Approximate Reasoning 40, 81–91 (2005)

    Article  MathSciNet  Google Scholar 

  13. Stefanowski, J., Tsoukias, A.: Incomplete information tables and rough classification. Comput. Intell. 17(3), 545–566 (2001)

    Article  Google Scholar 

  14. Wang, G.: Extension of rough set under incomplete information systems. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1098–1103 (2002)

    Google Scholar 

  15. Yao, Y.Y.: Probabilistic rough set approximations. Int. J. Approximate Reasoning 49, 255–271 (2008)

    Article  Google Scholar 

  16. Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximate concepts. Int. J. Man Mach. Studies 37, 793–809 (1992)

    Article  Google Scholar 

  17. Ziarko, W.: Variable precision rough set model. J. Comput. Sys. Sci. 46(1), 39–59 (1993)

    Article  MathSciNet  Google Scholar 

  18. Ziarko, W.: Probabilistic approach to rough sets. Int. J. Approximate Reasoning 49, 272–284 (2008)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Patrick G. Clark & Jerzy W. Grzymala-Busse

  2. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225, Rzeszow, Poland

    Jerzy W. Grzymala-Busse

Authors
  1. Patrick G. Clark
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  2. Jerzy W. Grzymala-Busse
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Corresponding author

Correspondence to Jerzy W. Grzymala-Busse .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  2. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  3. Indian Statistical Institute, Kolkata, India

    Sushmita Mitra

  4. Zhejiang Ocean University, Zhejiang, China

    Wei-Zhi Wu

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Clark, P.G., Grzymala-Busse, J.W. (2015). Mining Incomplete Data with Many Lost and Attribute-Concept Values. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-25754-9_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-25754-9

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