Abstract
In this paper we introduce an extension of the lazy learning method called Lazy Induction of Descriptions (LID). This new version is able to deal with fuzzy cases, i.e., cases described by attributes taking continuous values represented as fuzzy sets. LID classifies new cases based on the relevance of the attributes describing them. This relevance is assessed using a distance measure that compares the correct partition (i.e., the correct classification of cases) with the partitions induced by each one of the attributes. The fuzzy version of LID introduced in this paper uses two fuzzy versions of the Rand index to compare fuzzy partitions: one proposed by Campello and another proposed by Hüllermeier and Rifqi. We experimented with both indexes on data sets from the UCI machine learning repository.
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References
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–59 (1994)
Armengol, E., Plaza, E.: Bottom-up induction of feature terms. Machine Learning 41(1), 259–294 (2000)
Armengol, E., Plaza, E.: Lazy induction of descriptions for relational case-based learning. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 13–24. Springer, Heidelberg (2001)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Campello, R.J.G.B.: A fuzzy extension of the rand index and other related indexes for clustering and classification assessment. Pattern Recognition Letters 28(7), 833–841 (2007)
de Luca, A., Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control 20(4), 301–312 (1972)
López de Mántaras, R.: A distance-based attribute selection measure for decision tree induction. Machine Learning 6, 81–92 (1991)
Hüllermeier, E., Rifqi, M.: A fuzzy variant of the Rand index for comparing clustering structures. In: IFSA/EUSFLAT Conference, pp. 1294–1298 (2009)
Lebowitz, M.: Categorizing numeric information for generalization. Cognitive Science 9(3), 285–308 (1985)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846–850 (1971)
Schlimmer, J.C.: Learning and representation change. In: Proceedings of the AAAI 1987, pp. 511–515 (1987)
Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)
Witten, I., Frank, E., Trigg, L., Hall, M., Holmes, G., Cunningham, S.: Weka: Practical machine learning tools and techniques with java implementations (1999)
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Armengol, E., García-Cerdaña, À. (2010). Lazy Induction of Descriptions Using Two Fuzzy Versions of the Rand Index. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_41
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DOI: https://doi.org/10.1007/978-3-642-14055-6_41
Publisher Name: Springer, Berlin, Heidelberg
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