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If the discretization is poor, then the resulting feature evaluations will be affected. Tables 8.9 shows the results for data classi ed by x y z > 0.125. All feature rankers correctly detect these variables. However, in Table 8.10 the results can be seen for the same inequality but with the impact of variable z increased. All metrics determine that z has the most in uence on the decision, and almost all choose x and y next. Again, the 1R measure fails and chooses features 15, 19, and 24 instead. Only the FR and Re metrics are applicable to datasets where the decision feature is continuous. Results from the application of these two measures to such data can be found in Appendix B. Both methods nd the features that are involved in generating the decision values. This short investigation into the utility of the new fuzzy-rough measure has shown that it is comparable with the leading measures of feature importance. 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