Skip to main content
SpringerLink
Log in

A Proposal of Machine Learning by Rule Generation from Tables with Non-deterministic Information and Its Prototype System

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Included in the following conference series:

  • 1068 Accesses

Abstract

A logical framework on Machine Learning by Rule Generation (MLRG) from tables with non-deterministic information is proposed, and its prototype system in SQL is implemented. In MLRG, the certain rules defined in Rough Non-deterministic Information Analysis (RNIA) are obtained at first, and each uncertain attribute value is estimated so as to cause the certain rules as many as possible, because the certain rules show us the most reliable information. This strategy is similar to the maximum likelihood estimation in statistics. By repeating this process, a standard table and the rules in its table are learned (or estimated) from a given table with non-deterministic information. Even though it will be hard to know the actual unknown values, MLRG will give a plausible estimation value.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of VLDB 1994, pp. 487–499. Morgan Kaufmann (1994)

    Google Scholar 

  2. Aldrich, J.: R.A. Fisher and the making of maximum likelihood 1912–1922. Stat. Sci. 12(3), 162–176 (1997)

    Article  MathSciNet  Google Scholar 

  3. Clark, P., Grzymala-Busse, J.: Mining incomplete data with many attribute-concept values and “do not care” conditions. In: Proceedings of IEEE Big Data 2015, pp. 1597–1602 (2015)

    Google Scholar 

  4. Frank, A., Asuncion, A.: UCI machine learning repository. School of Information and Computer Science, University of California, Irvine (2010). http://mlearn.ics.uci.edu/MLRepository.html

  5. Grzymala-Busse, J.: Data with missing attribute values: generalization of indiscernibility relation and rule induction. Trans. Rough Sets 1, 78–95 (2004)

    MATH  Google Scholar 

  6. Kowalski, M., Stawicki, S.: SQL-based heuristics for selected KDD tasks over large data sets. In: Proceedings of FedCSIS 2012, pp. 303–310 (2012)

    Google Scholar 

  7. Kryszkiewicz, M.: Rules in incomplete information systems. Inf. Sci. 113(3–4), 271–292 (1999)

    Article  MathSciNet  Google Scholar 

  8. Lipski, W.: On databases with incomplete information. J. ACM 28(1), 41–70 (1981)

    Article  MathSciNet  Google Scholar 

  9. Orłowska, E., Pawlak, Z.: Representation of nondeterministic information. Theor. Comput. Sci. 29(1–2), 27–39 (1984)

    Article  MathSciNet  Google Scholar 

  10. Pawlak, Z.: Systemy Informacyjne: Podstawy Teoretyczne (in Polish) WNT (1983)

    Google Scholar 

  11. Sahri, Z., Yusof, R., Watada, J.: FINNIM: iterative imputation of missing values in dissolved gas analysis dataset. IEEE Trans. Ind. Inform. 10(4), 2093–2102 (2014)

    Article  Google Scholar 

  12. Sakai, H., et al.: Rules and apriori algorithm in non-deterministic information systems. Trans. Rough Sets 9, 328–350 (2008)

    MATH  Google Scholar 

  13. Sakai, H., Wu, M., Nakata, M.: Apriori-based rule generation in incomplete information databases and non-deterministic information systems. Fundam. Inform. 130(3), 343–376 (2014)

    MathSciNet  MATH  Google Scholar 

  14. Sakai, H., Wu, M.: The completeness of NIS-Apriori algorithm and a software tool getRNIA. In: Mori, M. (ed.) Proceedings of International Conference on AAI 2014, pp. 115–121. IEEE (2014)

    Google Scholar 

  15. Sakai, H., Liu, C.: A consideration on learning by rule generation from tables with missing values. In: Mine, T. (ed.) Proceedings of International Conference on AAI 2015, pp. 183–188. IEEE (2015)

    Google Scholar 

  16. Sakai, H., Liu, C., Zhu, X., Nakata, M.: On NIS-Apriori based data mining in SQL. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS (LNAI), vol. 9920, pp. 514–524. Springer, Cham (2016). doi:10.1007/978-3-319-47160-0_47

    Chapter  Google Scholar 

  17. Sakai, H.: Execution logs by RNIA software tools (2016). http://www.mns.kyutech.ac.jp/~sakai/RNIA

  18. Ślęzak, D., Sakai, H.: Automatic extraction of decision rules from non-deterministic data systems: theoretical foundations and SQL-based implementation. In: Ślęzak, D., Kim, T.H., Zhang, Y., Ma, J., Chung, K.I. (eds.) DTA 2009. CCIS, vol. 64, pp. 151–162. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10583-8_18

    Chapter  MATH  Google Scholar 

  19. Swieboda, W., Nguyen, S.: Rough set methods for large and spare data in EAV format. In: Proceedings of IEEE RIVF 2012, pp. 1–6 (2012)

    Google Scholar 

  20. Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180, 314–353 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

The authors would be grateful to the anonymous referees for their useful comments. This work is supported by JSPS (Japan Society for the Promotion of Science) KAKENHI Grant Number 26330277.

Author information

Authors and Affiliations

  1. Graduate School of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, 804-8550, Japan

    Hiroshi Sakai

  2. Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba, 283-0002, Japan

    Michinori Nakata

  3. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Junzo Watada

Authors
  1. Hiroshi Sakai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michinori Nakata
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junzo Watada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroshi Sakai .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Sakai, H., Nakata, M., Watada, J. (2017). A Proposal of Machine Learning by Rule Generation from Tables with Non-deterministic Information and Its Prototype System. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60837-2_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation