Abstract
Knowledge discovery in databases has traditionally focused on classification, prediction, or in the case of unsupervised discovery, clusters and class definitions. Equally important, however, is the discovery of individual predictors along a continuum of some metric that indicates their association with a particular class. This paper reports on the use of an XCS learning classifier system for this purpose. Conducted over a range of odds ratios for a fixed variable in synthetic data, it was found that XCS discovers rules that contain metric information about specific predictors and their relationship to a given class.
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References
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, Springer, Heidelberg (2001)
Carvalho, D.R., Freitas, A.A., Ebecken, N.F.F.: A critical review of rule surprisingness measures. In: Proc. Data Mining IV - Int. Conf. on Data Mining, Rio de Janeiro, Brazil, Dec. 2003, pp. 545–556. WIT Press (2003)
Fabris, C.C., Freitas, A.A.: Discovering surprising instances of Simpson’s paradox in hierarchical multidimensional data. To appear in Int. Journal of Data Warehousing and Mining 2(1), 26-48 (2006)
Freitas, A.A.: On objective measures of rule surprisingness. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 1–9. Springer, Heidelberg (1998)
Holmes, J.H.: The architecture of EpiXCS: An XCS-based learning classifier system for epidemiologic research. In: Sixth International Workshop on Learning Classifier Systems, Chicago, IL (July 2003)
Hulley, S.B., et al.: Designing Clinical Research, pp. 122–123. Lippincott Williams and Wilkins, Philadelphia (2001)
Lanzi, P.L.: http://xcslib.sourceforge.net/
STATA Corporation: STATA/SE 9.0 for Windows. College Station, TX
Wilson, S.W.: Compact Rulesets from XCSI. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 197–210. Springer, Heidelberg (2002)
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Holmes, J.H. (2007). Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis. In: Kovacs, T., Llorà , X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_18
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DOI: https://doi.org/10.1007/978-3-540-71231-2_18
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