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. In addition, EpiXCS performs qualitatively similarly to See5, and both methods are comparable to logistic regression. |
