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Governance of the Redundancy in the Feature Selection Based on Rough Sets’ Reducts

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Rough Sets (IJCRS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9920))

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Abstract

In this paper we introduced a novel approach to feature selection based on the theory of rough sets. We defined the concept of redundant reducts, whereby data analysts can limit the size of data and control the level of redundancy in generated subsets of attributes while maintaining the discernibility of all objects even in the case of partial data loss. What more, in the article we provide the analysis of the computational complexity and the proof of NP-hardness of the n-redundant super-reduct problem.

This research was partially supported by Polish National Centre for Research and Development (NCBiR) grant PBS2/B9/20/2013 in frame of Applied Research Programme.

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References

  1. Kozielski, M., Sikora, M., Wróbel, L.: DISESOR - decision support system for mining industry. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, 13–16 September 2015, pp. 67–74. IEEE (2015)

    Google Scholar 

  2. Grzegorowski, M., Stawicki, S.: Window-based feature engineering for prediction of methane threats in coal mines. [25], pp. 452–463

    Google Scholar 

  3. Janusz, A., Sikora, M., Wróbel, Ł., Stawicki, S., Grzegorowski, M., Wojtas, P., Ślęzak, D.: Mining data from coal mines: IJCRS ’15 data challenge. [25], pp. 429–438

    Google Scholar 

  4. Janusz, A., Ślęzak, D.: Rough set methods for attribute clustering and selection. Appl. Artif. Intell. 28(3), 220–242 (2014)

    Article  Google Scholar 

  5. Wasilewski, P., Ślęzak, D.: Foundations of rough sets from vagueness perspective. In: Rough Computing: Theories, Technologies and Applications. IGI Global (2008)

    Google Scholar 

  6. Zhong, N., Skowron, A.: A rough set-based knowledge discovery process. Appl. Math. Comput. Sci. 11(3), 603–620 (2001)

    MathSciNet  MATH  Google Scholar 

  7. Hu, X.: Ensembles of classifiers based on rough sets theory and set-oriented database operations. In: 2006 IEEE International Conference on Granular Computing, GrC 2006, Atlanta, Georgia, USA, 10–12 May 2006, pp. 67–73. IEEE (2006)

    Google Scholar 

  8. Yu, D., Hu, Q., Bao, W.: Combining multiple neural networks for classification based on rough set reduction. In: 2003 Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, vol. 1, pp. 543–548, December 2003

    Google Scholar 

  9. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Inf. Sci. 177(1), 41–73 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  10. Wroblewski, J.: Ensembles of classifiers based on approximate reducts. Fundam. Inf. 47(3–4), 351–360 (2001)

    MathSciNet  MATH  Google Scholar 

  11. Ślęzak, D., Janusz, A.: Ensembles of bireducts: towards robust classification and simple representation. In: Kim, T., Adeli, H., Ślęzak, D., Sandnes, F.E., Song, X., Chung, K., Arnett, K.P. (eds.) FGIT 2011. LNCS, vol. 7105, pp. 64–77. Springer, Heidelberg (2011). doi:10.1007/978-3-642-27142-7_9

    Chapter  Google Scholar 

  12. Grzegorowski, M.: Massively parallel feature extraction framework application in predicting dangerous seismic events. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of FedCSIS 2016. IEEE, September 2016 (In print)

    Google Scholar 

  13. Pawlak, Z.: Information systems, theoretical foundations. Inf. Syst. 3(6), 205–218 (1981)

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  15. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  16. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  17. Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problems. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56, pp. 49–88. Physica-Verlag GmbH, Heidelberg (2000)

    Chapter  Google Scholar 

  18. Janusz, A., Ślęzak, D.: Utilization of attribute clustering methods for scalable computation of reducts from high-dimensional data. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of Federated Conference on Computer Science and Information Systems - FedCSIS 2012, Wroclaw, Poland, 9–12 September 2012, pp. 295–302 (2012)

    Google Scholar 

  19. Ślęzak, D., Widz, S.: Rough-set-inspired feature subset selection, classifier construction, and rule aggregation. [26], pp. 81–88

    Google Scholar 

  20. Janusz, A., Ślęzak, D.: Computation of approximate reducts with dynamically adjusted approximation threshold. In: Esposito, F., Pivert, O., Hacid, M.-S., Raś, Z.W., Ferilli, S. (eds.) ISMIS 2015. LNCS (LNAI), vol. 9384, pp. 19–28. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25252-0_3

    Chapter  Google Scholar 

  21. Janusz, A., Stawicki, S.: Applications of approximate reducts to the feature selection problem. [26], pp. 45–50

    Google Scholar 

  22. Nguyen, H.S., Ślęzak, D.: Approximate reducts and association rules. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 137–145. Springer, Heidelberg (1999). doi:10.1007/978-3-540-48061-7_18

    Chapter  Google Scholar 

  23. Bazan, J.G., Skowron, A., Synak, P.: Dynamic reducts as a tool for extracting laws from decisions tables. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 346–355. Springer, Heidelberg (1994). doi:10.1007/3-540-58495-1_35

    Chapter  Google Scholar 

  24. Stawicki, S., Ślęzak, D.: Recent advances in decision bireducts: complexity, heuristics and streams. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 200–212. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41299-8_19

    Chapter  Google Scholar 

  25. Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.): RSFDGrC 2015. LNCS (LNAI), vol. 9437. Springer, Heidelberg (2015)

    Google Scholar 

  26. Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.): RSKT 2011. LNCS (LNAI), vol. 6954. Springer, Heidelberg (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Marek Grzegorowski

Authors
  1. Marek Grzegorowski
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Correspondence to Marek Grzegorowski .

Editor information

Editors and Affiliations

  1. Universidad Católica del Norte, Antofagasta, Chile

    Víctor Flores

  2. University of Campinas, Campinas, SP, Brazil

    Fernando Gomide

  3. University of Warsaw, Warsaw, Poland

    Andrzej Janusz

  4. Universidad del Católica del Norte, Antofagasta, Chile

    Claudio Meneses

  5. Tongji University, Shanghai, China

    Duoqian Miao

  6. University of Applied Sciences, Munich, Germany

    Georg Peters

  7. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

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

    Guoyin Wang

  9. Universidad de Chile, Santiago, Chile

    Richard Weber

  10. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

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Grzegorowski, M. (2016). Governance of the Redundancy in the Feature Selection Based on Rough Sets’ Reducts. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_50

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  • DOI: https://doi.org/10.1007/978-3-319-47160-0_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47159-4

  • Online ISBN: 978-3-319-47160-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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