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International Conference on Database and Expert Systems Applications

DEXA 2018: Database and Expert Systems Applications pp 369–377Cite as

Features’ Associations in Fuzzy Ensemble Classifiers

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11030))

Abstract

In this work, we are interested in ensemble methods for fuzzy rule-based classification systems where the decisions of different classifiers are combined to form the final classification model. We focus on ensemble methods that cluster the set of attributes into subgroups and treat each subgroup separately. This allows decomposing the learning problem into sub-problems of lower complexity and obtaining more intelligible rules as their number and size are smaller. In this paper, we study different methods that allow finding associations between the attributes. In this context, SIFRA is an interesting attributes regrouping method based on association rules concept. We compare SIFRA with some other association methods and show, via a detailed analysis of experimental results, that it is able to find interesting types of associations including linear and non-linear ones. Moreover, it improves the system’s accuracy and guarantees a smaller rules number compared to classical FRBCS.

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Notes

  1. 1.

    https://www.rstudio.com/products/RStudio/.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets.html.

  3. 3.

    https://weka.wikispaces.com/Related+Projects.

References

  1. Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Set Syst. 52(1), 21–32 (1992)

    Article  Google Scholar 

  2. Dehzangi, O., Zolghadri, M.J., Taheri, S., Fakhrahmad, S.M.: Efficient fuzzy rule generation: a new approach using data mining principles and rule weighting. In: Fuzzy Systems and Knowledge Discovery, FSKD 2007, vol. 2, pp. 134–139 (2007)

    Google Scholar 

  3. Rudziński, F.: A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Appl. Soft Comput. 38, 118–133 (2016)

    Article  Google Scholar 

  4. Alcalá, R., Gacto, M.J., Herrera, F., Alcalá-Fdez, J.: A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Uncertain. Fuzziness Knowl.-Based Syst. 15, 539–557 (2007)

    Article  Google Scholar 

  5. Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th International Conference on Machine Learning, pp. 856–863 (2003)

    Google Scholar 

  6. Borgi, A., Bazin, J.-M., Akdag, H.: Two methods of linear correlation search for a knowledge based supervised classification. In: Mira, J., del Pobil, A.P., Ali, M. (eds.) IEA/AIE 1998. LNCS, vol. 1415, pp. 696–707. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-64582-9_802

    Chapter  Google Scholar 

  7. Soua, B., Borgi, A., Tagina, M.: An ensemble method for fuzzy rule-based classification systems. Knowl. Inf. Syst. 36(2), 385–410 (2013)

    Article  Google Scholar 

  8. Ben Slima, I., Borgi, A.: Attributes regrouping by association rules in the fuzzy inference systems. Regroupement d’attributs par règles d’association dans les systèmes d’inférence floue. In: EGC 2015, Luxembourg, vol. RNTI-E-28, pp. 317–328 (2015)

    Google Scholar 

  9. Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Kaufmann, Burlington (2011)

    Google Scholar 

  10. Skurichina, M., Duin, R.: Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5(2), 121–135 (2002)

    Article  MathSciNet  Google Scholar 

  11. Saporta, G.: Probabilité, analyse des données et statistique, 2nd edn. Editions Technip, Paris Cedex (2006)

    MATH  Google Scholar 

  12. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th Very Large Data Bases Conference, VLDB 1994, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  13. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD Record, vol. 29, pp. 1–12 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculté des Sciences de Tunis, LIPAH, University of Tunis El Manar, 2092, Tunis, Tunisie

    Ilef Ben Slima

  2. Institut Supérieur d’Informatique, LIPAH, University of Tunis El Manar, 2080, Tunis, Tunisie

    Amel Borgi

Authors
  1. Ilef Ben Slima
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  2. Amel Borgi
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Corresponding author

Correspondence to Ilef Ben Slima .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

  3. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Regensburg, Regensburg, Germany

    Günther Pernul

  5. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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Ben Slima, I., Borgi, A. (2018). Features’ Associations in Fuzzy Ensemble Classifiers. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-98812-2_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98811-5

  • Online ISBN: 978-3-319-98812-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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