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FURIA: an algorithm for unordered fuzzy rule induction

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Abstract

This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.

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References

  • Aha D, Kibler D, Albert M (1991) Instance-based learning algorithms. Mach Learn 6(1): 37–66

    Google Scholar 

  • Alcalá-Fernandez J, Sánchez L, García S, del Jesus M, Ventura S, Garrell J, Otero J, Romero C, Bacardit J, Rivas V, Fernández J, Herrera F (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3): 307–318

    Article  Google Scholar 

  • Asuncion A, Newman D (2007) UCI machine learning repository. http://archive.ics.uci.edu/ml/index.html. Accessed 20 Aug 2007

  • Barker D (2007) Dataset: pasture production. http://weka.sourceforge.net/wiki/index.php/Datasets. Accessed 20 Oct 2007

  • Boström H (2004) Pruning and exclusion criteria for unordered incremental reduced error pruning. Proceedings of the workshop on advances in rule learning, ECML, pp 17–29

  • Bulloch B (2007) Dataset: eucalyptus soil conservation. http://weka.sourceforge.net/wiki/index.php/Datasets. Accessed 20 Oct 2007

  • Chi Z, Wu J, Yan H (1995) Handwritten numeral recognition using self-organizing maps and fuzzy rules. Pattern Recogn 28(1): 59–66

    Article  Google Scholar 

  • Chi Z, Yan H, Pham T (1996) Fuzzy algorithms: with applications to image processing and pattern recognition. World Scientific Publishing Co., Inc., River Edge

  • Cloete I, Van Zyl J (2006) Fuzzy rule induction in a set covering framework. IEEE Trans Fuzzy Syst 14(1): 93–110

    Article  Google Scholar 

  • Cohen W (1995) Fast effective rule induction. In: Prieditis A, Russell S (eds) Proceedings of the 12th international conference on machine learning, ICML. Morgan Kaufmann, Tahoe City, pp 115–123

  • Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141(1): 5–31

    Article  MathSciNet  MATH  Google Scholar 

  • del Jesus M, Hoffmann F, Navascues L, Sd́fnchez L (2004) Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans Fuzzy Syst 12(3): 296–308

    Article  Google Scholar 

  • Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7: 1–30

    MathSciNet  Google Scholar 

  • Domingos P (1995) Rule induction and instance-based learning: a unified approach. In: Proceedings of the fourteenth international joint conference on artificial intelligence, IJCAI. Morgan Kaufmann, Montral, pp 1226–1232

  • Drobics M, Bodenhofer U, Klement E (2003) FS-FOIL: an inductive learning method for extracting interpretable fuzzy descriptions. Int J Approx Reason 32(2–3): 131–152

    Article  MATH  Google Scholar 

  • Eineborg M, Boström H (2001) Classifying uncovered examples by rule stretching. In: ILP ’01: Proceedings of the 11th international conference on inductive logic programming. Springer-Verlag, London, pp 41–50

  • Fawcett T (2008) Prie: a system for generating rulelists to maximize roc performance. Data Min Knowl Discov 17(2): 207–224

    Article  Google Scholar 

  • Fernández A, García S, Herrera F, del Jesus M (2007) An analysis of the rule weights and fuzzy reasoning methods for linguistic rule based classification systems applied to problems with highly imbalanced data sets. In: Applications of fuzzy sets theory, vol 4578, Lecture notes in computer science. Springer, Berlin/Heidelberg, pp 170–178

  • Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200): 675–701

    Article  Google Scholar 

  • Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1): 86–92

    Article  MATH  Google Scholar 

  • Fürnkranz J (1999) Separate-and-conquer rule learning. Artif Intell Rev 13(1): 3–54

    Article  MATH  Google Scholar 

  • Fürnkranz J, Widmer G (1994) Incremental reduced error pruning. In: Proceedings of the 11th international conference on machine learning, ICML, pp 70–77

  • Gonzalez A (1999) Slave: a genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 7(2): 176–191

    Article  Google Scholar 

  • Gonzalez A (2001) Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans Syst Man Cyber B 31(3): 417–425

    Article  Google Scholar 

  • Guillaume S (2001) Defining fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans Fuzzy Syst 9(3): 426–443

    Article  MathSciNet  Google Scholar 

  • Harvey W (2007) Dataset: squash harvest stored/unstored. http://weka.sourceforge.net/wiki/index.php/Datasets. Accessed on 20 Oct 2007

  • Hendrickx I, van den Bosch A (2005) Hybrid algorithms for instance-based classification. In: Gama J, Camacho R, Brazdil P, Jorge A, Torgo L (eds) Proceedings of the sixteenth European conference on machine learning, ECML. Springer, Berlin, pp 158–169

  • Hühn J, Hüllermeier E (2009) FR3: a fuzzy rule learner for inducing reliable classifiers. IEEE Trans Fuzzy Syst 17(1): 138–149

    Article  Google Scholar 

  • Hüllermeier E (2005) Fuzzy sets in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156(3): 387–406

    Article  Google Scholar 

  • Iman R, Davenport J (1980) Approximations of the critical region of the friedman statistic. Commun Stat 9(6): 571–595

    Article  Google Scholar 

  • Ishibuchi H, Nakashima T (2001) Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 9(4): 506–515

    Article  Google Scholar 

  • Ishibuchi H, Yamamoto T (2003) Performance evaluation of three-objective genetic rule selection. In: The 12th IEEE international conference on fuzzy systems, vol 1, pp 149–154

  • Ishibuchi H, Yamamoto T (2005) Rule weight specification in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 13(4): 428–436

    Article  Google Scholar 

  • Juang C, Chiu S, Chang S (2007) A self-organizing ts-type fuzzy network with support vector learning and its application to classification problems. IEEE Trans Fuzzy Syst 15(5): 998–1008

    Article  Google Scholar 

  • Kamal M, Pazzani M (1993) Hydra: a noise-tolerant relational concept learning algorithm. In: Proceedings of the thirteenth international joint conference on artificial intelligence, IJCAI. Morgan Kaufmann, Chambry, pp 1064–1071

  • Kearns M (1988) Thoughts on hypothesis boosting. ML class project

  • Meyer M, Vlachos P (2007) Statlib.http://lib.stat.cmu.edu/

  • Mitra S, Hayashi Y (2000) Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw 11(3): 748–768

    Article  Google Scholar 

  • Nauck D, Klawonn F, Kruse R (1997) Foundations of neuro-fuzzy systems. Chichester, Wiley

    Google Scholar 

  • Nemenyi P (1963) Distribution-free multiple comparisons. PhD thesis, Princeton University

  • Newman D (1939) The distribution of range in samples from a normal population expressed in terms of an independent estimate of standard deviation. Biometrika 31: 20–30

    MathSciNet  MATH  Google Scholar 

  • Prade H, Richard G, Serrurier M (2003) Enriching relational learning with fuzzy predicates. In: Proceedings of PKDD–03 European conference on principles and practice of knowledge discovery in databases, pp 399–410

  • Press W, Flannery B, Teukolsky S, Vetterling W (1992) Numerical recipes in FORTRAN: the art of scientific computing, 2nd edn. Cambridge University Press

  • Quinlan J (1990) Learning logical definitions from relations. Mach Learn 5(3): 239–266

    Google Scholar 

  • Quinlan J (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Quinlan J (1995) MDL and categorial theories (continued). In: Proceedings of the 12th international conference on machine learning, ICML. Morgan Kaufmann, Lake Tahoe, pp 464–470

  • Quinlan J, Cameron-Jones R (1993) Foil: a midterm report. In: Proceedings of the 6th European conference on machine learning, ECML. Springer-Verlag, London, pp 3–20

  • Serrurier M, Prade H (2007) Introducing possibilistic logic in ILP for dealing with exceptions. Artif Intell 171(16–17): 939–950

    Article  MathSciNet  MATH  Google Scholar 

  • Wang L, Mendel J (1992) Generating fuzzy rules by learning from examples. Trans Syst Man Cyber 22(6): 1414–1427

    Article  MathSciNet  Google Scholar 

  • Wang T, Li Z, Yan Y, Chen H (2007) A survey of fuzzy decision tree classifier methodology. In: Proceedings of the second international conference of fuzzy information and engineering, vol 40 of advances in soft computing. Springer-Verlag, Berlin/Heidelberg

  • Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco

    MATH  Google Scholar 

  • Zolghadri M, Mansoori E (2007) Weighting fuzzy classification rules using receiver operating characteristics (roc) analysis. Inf Sci 177(11): 2296–2307

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Philipps-Universität Marburg, Mehrzweckgebäude, Hans-Meerwein-Straße, Marburg, 35043, Germany

    Jens Hühn & Eyke Hüllermeier

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  1. Jens Hühn
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  2. Eyke Hüllermeier
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Correspondence to Eyke Hüllermeier.

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Responsible editor: Charles Elkan.

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Hühn, J., Hüllermeier, E. FURIA: an algorithm for unordered fuzzy rule induction. Data Min Knowl Disc 19, 293–319 (2009). https://doi.org/10.1007/s10618-009-0131-8

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