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MLEM2 Rule Induction Algorithm with Multiple Scanning Discretization

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Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 72))

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Abstract

In this paper we show experimental results on the MLEM2 rule induction algorithm and the Multiple Scanning discretization algorithm. The MLEM2 algorithm of rule induction has its own mechanisms to handle missing attribute values and numerical data. We compare, in terms of an error rate, two setups: MLEM2 used for rule induction directly from incomplete and numerical data and MLEM2 inducing rule sets from data sets previously discretized by Multiple Scanning and then converted to be incomplete. In both setups certain and possible rule sets were induced. For certain rule sets, the former setup was more successful for two data sets, while the latter setup was more successful for four data sets, for eight data sets the difference was not significant (Wilcoxon test, 5% significance level). Similarly, for possible rule sets the former setup was more successful for two data sets, while the latter setup was more successful for three data sets. Thus we may conclude that there is not significant difference between both setups and that we may use MLEM2 for rule induction directly from incomplete and numerical data.

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References

  1. Blajdo, P., Grzymala-Busse, J.W., Hippe, Z.S., Knap, M., Mroczek, T., Piatek, L.: A comparison of six approaches to discretization—a rough set perspective. In: Proceedings of the Rough Sets and Knowledge Technology Conference, pp. 31–38 (2008)

    Google Scholar 

  2. Chan, C.C., Batur, C., Srinivasan, A.: Determination of quantization intervals in rule based model for dynamic. In: Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, pp. 1719–1723 (1991)

    Google Scholar 

  3. Chmielewski, M.R., Grzymala-Busse, J.W.: Global discretization of continuous attributes as preprocessing for machine learning. Int. J. Approximate Reasoning 15(4), 319–331 (1996)

    Article  MATH  Google Scholar 

  4. Clarke, E.J., Barton, B.A.: Entropy and MDL discretization of continuous variables for bayesian belief networks. Int. J. Intell. Syst. 15, 61–92 (2000)

    Article  Google Scholar 

  5. Elomaa, T., Rousu, J.: General and efficient multisplitting of numerical attributes. Mach. Learn. 36, 201–244 (1999)

    Article  MATH  Google Scholar 

  6. Elomaa, T., Rousu, J.: Efficient multisplitting revisited: optima-preserving elimination of partition candidates. Data Min. Knowl. Disc. 8, 97–126 (2004)

    Article  MathSciNet  Google Scholar 

  7. Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8, 87–102 (1992)

    MATH  Google Scholar 

  8. Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence, pp. 1022–1027 (1993)

    Google Scholar 

  9. Grzymala-Busse, J.W.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27–39 (1997)

    MATH  Google Scholar 

  10. Grzymala-Busse, J.W.: MLEM2—discretization during rule induction. In: Proceedings of the International Conference on Intelligent Information Processing and WEB Mining Systems, pp. 499–508 (2003)

    Google Scholar 

  11. Grzymala-Busse, J.W.: Rough set strategies to data with missing attribute values. In: Notes of the Workshop on Foundations and New Directions of Data Mining, in Conjunction with the Third International Conference on Data Mining, pp. 56–63 (2003)

    Google Scholar 

  12. Grzymala-Busse, J.W.: Characteristic relations for incomplete data: a generalization of the indiscernibility relation. In: Proceedings of the Fourth International Conference on Rough Sets and Current Trends in Computing, pp. 244–253 (2004)

    Google Scholar 

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

    MATH  Google Scholar 

  14. Grzymala-Busse, J.W.: Three approaches to missing attribute values—a rough set perspective. In: Proceedings of the Workshop on Foundation of Data Mining, in Conjunction with the Fourth IEEE International Conference on Data Mining, pp. 55–62 (2004)

    Google Scholar 

  15. Grzymala-Busse, J.W.: A multiple scanning strategy for entropy based discretization. In: Proceedings of the 18th International Symposium on Methodologies for Intelligent Systems, pp. 25–34 (2009)

    Google Scholar 

  16. Grzymala-Busse, J.W.: Discretization based on entropy and multiple scanning. Entropy 15, 1486–1502 (2013)

    Article  MathSciNet  Google Scholar 

  17. Grzymala-Busse, J.W., Mroczek, T.: A comparison of two approaches to discretization: multiple scanning and c4.5. In: Proceedings of the 6th International Conference on Pattern Recognition and Machine Learning, pp. 44–53 (2015)

    Google Scholar 

  18. Grzymala-Busse, J.W., Mroczek, T.: A comparison of four approaches to discretization based on entropy. Entropy 18, 1–11 (2016)

    Article  Google Scholar 

  19. Kohavi, R., Sahami, M.: Error-based and entropy-based discretization of continuous features. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 114–119 (1996)

    Google Scholar 

  20. Kotsiantis, S., Kanellopoulos, D.: Discretization techniques: a recent survey. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 47–58 (2006)

    Google Scholar 

  21. Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: an enabling technique. Data Min. Knowl. Disc. 6, 393–423 (2002)

    Article  MathSciNet  Google Scholar 

  22. Nguyen, H.S., Nguyen, S.H.: Discretization methods in data mining. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 451–482. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  23. Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)

    Article  MATH  Google Scholar 

  24. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  25. Pawlak, Z., Grzymala-Busse, J.W., Slowinski, R., Ziarko, W.: Rough sets. Commun. ACM 38, 89–95 (1995)

    Article  Google Scholar 

  26. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)

    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, Cheng Gao & 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. Cheng Gao
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  3. Jerzy W. Grzymala-Busse
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Corresponding author

Correspondence to Jerzy W. Grzymala-Busse .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Clark, P.G., Gao, C., Grzymala-Busse, J.W. (2018). MLEM2 Rule Induction Algorithm with Multiple Scanning Discretization. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-59421-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59420-0

  • Online ISBN: 978-3-319-59421-7

  • eBook Packages: EngineeringEngineering (R0)

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