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Novel Granular Framework for Attribute Reduction in Incomplete Decision Systems

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7694))

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Abstract

Incomplete decision systems containing missing attribute values occur frequently in real world applications. This paper proposes IQRAIG_incomplete algorithm for reduct computation in Incomplete Decision Systems using a novel granular framework. The proposed granular framework enables computation of similarity classes for a set of objects simultaneously which helps in increased effienciency of computing positive region. The merits of the proposed algorithm over IFSPA-IPR algorithm has been demonstrated empirically.

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References

  1. Chouchoulas, A., Shen, Q.: Rough Set Aided Keyword Reduction for Text Categorization. Applied Artificial Intelligence 15, 843–873 (2001)

    Article  Google Scholar 

  2. Grzymała-Busse, J.W., Hu, M.: A Comparison of Several Approaches to Missing Attribute Values in Data Mining. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 378–385. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  3. Jensen, R., Shen, Q.: Rough set based feature selection: A review. In: Rough Computing: Theories, Technologies and Applications, pp. 70–107. IGI global (2007)

    Google Scholar 

  4. Kryszkiewicz, M.: Rough set approach to incomplete information systems. Information Sciences 112, 39–49 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  5. Leung, Y., Li, D.: Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences 153, 85–106 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  6. Liang, J., Xu, Z.: The Algorithm on Knowledge Reduction in Incomplete Information Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(1), 95–103 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  7. Meng, Z., Shi, Z.: A fast approach to attribute reduction in incomplete decision systems with tolerance relation based rough sets. Information Sciences 179, 2774–2793 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Nakata, M., Sakai, H.: Rough Sets Handling Missing Values Probabilistically Interpreted. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 325–334. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Nakata, M., Sakai, H.: Applying Rough Sets to Data Tables Containing Missing Values. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 181–191. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Nguyen, H.S., Skowron, A.: Boolean Reasoning for Feature Extraction Problems. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1997. LNCS (LNAI), vol. 1325, pp. 117–126. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  11. Pawlak, Z.: Rough Sets. International Journal of Computer and Information Science 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  12. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of ACM 38(11), 89–95 (1995)

    Article  Google Scholar 

  13. Polkowski, L., Artiemjew, P.: On Granular Rough Computing with Missing Values. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 271–279. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Qian, Y., Liang, J., Pedrycz, W., Dang, C.: An efficient accelerator for attribute reduction from incomplete data in rough set framework. Pattern Recognition 44, 1658–1670 (2011)

    Article  MATH  Google Scholar 

  15. Sai Prasad, P.S.V.S., Raghavendra Rao, C.: Extensions to IQuickReduct. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI 2011. LNCS (LNAI), vol. 7080, pp. 351–362. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  16. Stefanowski, J., Tsoukiàs, A.: On the Extension of Rough Sets under Incomplete Information. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 73–82. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  17. Sun, L., Xu, J., Li, S., Cao, X., Gao, Y.: New Approch for Feature Selection by Using Information Entropy. Journal of Information & Computational Science 8(12), 2259–2268 (2011)

    Google Scholar 

  18. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html

  19. Wang, G.: Extension of rough set under incomplete information systems. In: Fuzzy Systems, FUZZ-IEEE 2002, vol. 2, pp. 1098–1103 (2002)

    Google Scholar 

  20. Zhang, Q., Zheng, X., Xu, Z.: Efficient Attribute Reduction Algorithm Based on Incomplete Decision Table. In: 2nd International Conference on Intelligent Computational Technology and Automation, pp. 192–195. IEEE (2009)

    Google Scholar 

  21. Zhao, B., Chen, X., Zeng, Q.: Incomplete Hybrid Attributes Reduction Based on Neighborhood Granulation and Approximation. In: International Converence on Mechatronics and Automation, pp. 2066–2071. IEEE (2009)

    Google Scholar 

  22. Zou, H., Zhang, C.: Efficient Algorithm for Knowledge Reduction in Incomplete Information Systems. Journal of Computational Information Systems 8, 2531–2538 (2012)

    Google Scholar 

  23. Van, N.D., Yamada, K., Unehara, M.: Knowledge reduction in incomplete decision tables using Probabilistic Similarity-Based Rough set Model. In: ISIS 2011, pp. 147–150 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    Sai Prasad P.S.V.S. & Raghavendra Rao Chillarige

Authors
  1. Sai Prasad P.S.V.S.
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  2. Raghavendra Rao Chillarige
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Editor information

Editors and Affiliations

  1. Faculty of Informatics, Mahasarakham University, Khamreang, Kantarawichai, 44150, Mahasarakham, Thailand

    Chattrakul Sombattheera

  2. Nong Lam University, Linh Trung ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Nguyen Kim Loi

  3. Department of Computer and Information Sciences, University of Hyderabad, P. O. Central University, Gachibowli, 500 046, Hyderabad, India

    Rajeev Wankar

  4. Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh, Vietnam

    Tho Quan

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© 2012 Springer-Verlag Berlin Heidelberg

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P.S.V.S., S.P., Chillarige, R.R. (2012). Novel Granular Framework for Attribute Reduction in Incomplete Decision Systems. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_18

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  • DOI: https://doi.org/10.1007/978-3-642-35455-7_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35454-0

  • Online ISBN: 978-3-642-35455-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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