Skip to main content
Springer Nature Link
Log in

Rough Sets-Based Machine Learning over Non-deterministic Data: A Brief Survey

  • Conference paper
Advanced Machine Learning Technologies and Applications (AMLTA 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 322))

  • 3342 Accesses

Abstract

Rough Non-deterministic Information Analysis (RNIA) is a rough sets-based framework for handling tables with exact and inexact data. Under this framework, we investigated possible equivalence relations, data dependencies, rule generation, rule stability, question-answering systems, as well as missing and interval values as special cases of non-deterministic values. In this paper, we briefly survey RNIA, and report the state of its underlying software implementation. We also discuss to what extent RNIA can be seen as an example of a new emerging paradigm in machine learning.

This work is supported by the Grant-in-Aid for Scientific Research (C) (No.22500204), Japan Society for the Promotion of Science. The fourth author was partially supported by the Polish National Science Centre grant 2011/01/B/ST6/03867.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB 1994, pp. 487–499. Morgan Kaufmann (1994)

    Google Scholar 

  2. Grzymała-Busse, J.W.: Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 78–95. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Kryszkiewicz, M.: Rules in incomplete information systems. Information Sciences 113(3-4), 271–292 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  4. Lipski, W.: On databases with incomplete information. Journal of the ACM 28(1), 41–70 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  5. Nakata, M., Sakai, H.: Applying Rough Sets to Information Tables Containing Possibilistic Values. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Yao, Y., Wang, G. (eds.) Transactions on Computational Science II. LNCS, vol. 5150, pp. 180–204. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Orłowska, E., Pawlak, Z.: Representation of nondeterministic information. Theoretical Computer Science 29(1-2), 27–39 (1984)

    Article  MathSciNet  Google Scholar 

  7. Pawlak, Z.: Systemy Informacyjne: Podstawy Teoretyczne. WNT (1983) (in Polish; English translation: Information Systems: Theoretical Foundations)

    Google Scholar 

  8. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers (1991)

    Google Scholar 

  9. Peters, G., Lingras, P., Ślęzak, D., Yao, Y. (eds.): Selected Methods and Applications of Rough Sets in Management and Engineering. Springer (2012)

    Google Scholar 

  10. Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery, Parts 1 & 2. Physica-Verlag (1998)

    Google Scholar 

  11. RNIA software logs, http://www.mns.kyutech.ac.jp/~sakai/RNIA

  12. Sakai, H.: Possible Equivalence Relations and Their Application to Hypothesis Generation in Non-deterministic Information Systems. In: Peters, J.F., Skowron, A., Dubois, D., Grzymała-Busse, J.W., Inuiguchi, M., Polkowski, L. (eds.) Transactions on Rough Sets II. LNCS, vol. 3135, pp. 82–106. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  13. Sakai, H., Ishibashi, R., Koba, K., Nakata, M.: Rules and Apriori Algorithm in Non-deterministic Information Systems. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 328–350. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. Sakai, H., Koba, K., Nakata, M.: Rough sets based rule generation from data with categorical and numerical values. Journal of Advanced Computational Intelligence and Intelligent Informatics 12(5), 426–434 (2008)

    Google Scholar 

  15. Sakai, H., Okuma, A.: Basic Algorithms and Tools for Rough Non-deterministic Information Analysis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 209–231. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  16. Sakai, H., Nakata, M., Ślęzak, D.: A Prototype System for Rule Generation in Lipski’s Incomplete Information Databases. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 175–182. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  17. Sakai, H., Okuma, H., Nakata, M., Ślęzak, D.: Stable rule extraction and decision making in rough non-deterministic information analysis. International Journal of Hybrid Intelligent Systems 8(1), 41–57 (2011)

    MATH  Google Scholar 

  18. UCI Machine Learning Repository, http://mlearn.ics.uci.edu/MLRepository.html

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, 804, Japan

    Hiroshi Sakai & Mao Wu

  2. Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba, 283, Japan

    Michinori Nakata

  3. Institute of Mathematics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Dominik Ślęzak

  4. Infobright Inc., Krzywickiego 34 pok. 219, 02-078, Warsaw, Poland

    Dominik Ślęzak

Authors
  1. Hiroshi Sakai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michinori Nakata
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dominik Ślęzak
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Cairo University, Egypt

    Aboul Ella Hassanien  & Rabie Ramadan  & 

  2. Ain Shams University, Cairo, Egypt

    Abdel-Badeeh M. Salem

  3. University of Tasmania, TAS, Australia

    Tai-hoon Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sakai, H., Wu, M., Nakata, M., Ślęzak, D. (2012). Rough Sets-Based Machine Learning over Non-deterministic Data: A Brief Survey. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35326-0_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35325-3

  • Online ISBN: 978-3-642-35326-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics