By Hisao Ishibuchi
Many methods have already been proposed for category and modeling within the literature. those methods tend to be in response to mathematical mod els. desktops can simply deal with mathematical versions even if they're complex and nonlinear (e.g., neural networks). however, it's not continuously effortless for human clients to intuitively comprehend mathe matical versions even if they're easy and linear. the reason is, human details processing is predicated usually on linguistic wisdom whereas com puter structures are designed to address symbolic and numerical info. a wide a part of our day-by-day communique is predicated on phrases. We examine from a number of media resembling books, newspapers, magazines, television, and the Inter web via phrases. We additionally speak with others via phrases. whereas phrases play a significant position in human info processing, linguistic versions aren't frequently utilized in the fields of class and modeling. If there isn't any objective except the maximization of accuracy in class and version ing, mathematical versions may perhaps continuously be most well-liked to linguistic types. nevertheless, linguistic types will be selected if emphasis is put on interpretability.
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Additional info for Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining
The same results were obtained with respect to the number of generated linguistic rules from the second and third definitions ^crranuiarity K K K K = = = = 2 3 4 5 Length 0 1 1 1 1 Length of linguistic rules (L) Length 1 Length 2 Length 3 312 26 2288 39 7585 701 52 15766 1201 65 25589 1768 Length 4 11440 54633 129624 220876 one-out technique. 9. 8. Classification rates on training patterns in the wine data set. 7% result in this table Rule weight definition 1st def. 2nd def. 3rd def. 4% 4th def.
11 and Fig. 12, we can see t h a t the rule weight of each linguistic rule has a large effect on the classification boundary. Note t h a t we did not modify the membership function of each linguistic t e r m in these figures. 0 (a) Single winner-based method. (b) Voting-based method. Fig. 11. 0 (a) Single winner-based method. (b) Voting-based method. Fig. 12. 4 Computer Simulations We compare the four definitions of rule weights with one another through computer simulations on a class of simple artificial test problems and two well-known real-world d a t a sets: iris d a t a and wine data.
Step 2: Classify the training pattern by the linguistic rule-based system. Step 3: When the training pattern is misclassified, perform the following procedures. Otherwise, go to Step 4. 13). 13) is iterated until the current training pattern chosen in Step 1 is correctly classified. If no modified versions outperform the current one, go to Step 4. (2) Replace the current linguistic rule-based system with the better modified version. If the two modified versions have the same classification rate on training patterns, randomly choose one version.
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