### Table 4: Fuzzy query evaluation in the testing set

"... In PAGE 19: ... The KFC-L showed the best performance for the image data set, but for the ringnorm data set, the KFC-L showed a large error compared to other methods. Table4 shows the best recognition rates of the ringnorm validation data sets. The recognition rates of the KFC-L are very low com- pared to those of the KFC.... In PAGE 20: ...Table4 . Cross validation results (%) for the ringnorm data set.... In PAGE 29: ... The Precision and Recall metrics computed by the J4.8 algorithm over the same testing set are also shown in Table4 . It can be seen that the fuzzy query results over the testing set correspond roughly to the ones computed from the training set.... In PAGE 48: ...ig. 7. Comparison between heuristic rule extraction and genetics- bases multiobjective rule selection with respect to the average error rates on training patterns (Wine). Wisconsin Breast Cancer Data: Experimental results by heuristic rule extraction are summarized in Table4 where the average error rate on test patterns over three iterations of the 10CV procedure is shown for each combination of a heuristic rule extraction criterion and the number of extracted rules for each class. The best error rate in each row is indicated by bold face.... In PAGE 48: ...5 algorithm with respect to the generalization ability. Table4 . Average error rates on test patterns of extracted rules by heuristic rule extraction (Breast W).... In PAGE 86: ...007 isdn .009 Table4 . Continuation of onramp/offramp traf- fic distribution for Figure 5.... ..."

### Table 4.2 The Structure of a Single Gene Representing One Fuzzy Variable with 4 Fuzzy Sets as Values

1998

Cited by 1

### Table 1: Preference Relation of Fuzzy Sets

"... In PAGE 6: ... Now we use an aggregation function for combining the fuzzy sets on the BLTS to obtain a collective performance for each alternative that will be a fuzzy set on the BLTS. For the heterogeneous GDM problem, the prefer- ence relations are expressed by means of fuzzy sets on the BLTS as can be seen in Table1 , where D4 CZ CXCY is the preference degree of the alternative DC CX over DC CY provides by the expert CT CZ . We shall represent each fuzzy set, D4 CZ CXCY ,asD6 CZ CXCY BP B4AD CXCY CZ BC BNBMBMBMBNAD CXCY CZ CV B5 being the values of D6 CZ CXCY their respec- tive membership degrees.... ..."

### Table 11: Selected machine learning results for the secondary-structure prediction task. Due to di ering data sets and experimental methodologies, these numbers can only be roughly compared.

1995

"... In PAGE 46: ...ost, 1992), Zhang et al. apos;s (1992) hybrid approach, and Leng et al. apos;s (1993) approach. Table11 show that these approaches achieve signi cant improvement over approaches using only neural networks. Finally, the work of Rost and Sander (1993), which signi cantly reformulates the input representation of the task, produces 70.... ..."

Cited by 15

### Table 3. The Sequential Encoding of Lattice Elements It is easy to show the following facts, by induction over the type structure:

"... In PAGE 7: ...Table3... ..."

### Table 1. Topology correction on a set of brains

"... In PAGE 6: ... The corrected membership functions are very close to the original ones (cf. Table1 ): the number of changed voxels over the entire image is around 10%, and the mean amount of change for these voxels (counting only changed voxels) is below 0.05 (the membership intensities are in [0, 1]).... ..."

Cited by 1

### Table 2 Classical rough set-based feature selection where numerical attributes are discretized Data Feature CART

2006

"... In PAGE 7: ...introduced to evaluate the quality of selected features. Table2 shows the results with classical rough method [36], where N1 and N2 mean number of features in original data and reduced data, respectively. Table 3 gives the results based on fuzzy information entropy reduction.... ..."

### Table 2: Lattice Semantics of the First Order Formula of Fuzzy Monoidal Logics

2003