By Hisao Ishibuchi
Many methods have already been proposed for class and modeling within the literature. those techniques are typically in line with mathematical mod els. computers can simply deal with mathematical versions even if they're complex and nonlinear (e.g., neural networks). nonetheless, it isn't continually effortless for human clients to intuitively comprehend mathe matical versions even if they're basic and linear. it's because human info processing relies mostly on linguistic wisdom whereas com puter structures are designed to deal with symbolic and numerical details. a wide a part of our day-by-day communique relies on phrases. We research from quite a few media corresponding to books, newspapers, magazines, television, and the Inter internet via phrases. We additionally speak with others via phrases. whereas phrases play a critical position in human details processing, linguistic types are usually not usually utilized in the fields of type and modeling. If there's no target except the maximization of accuracy in category and version ing, mathematical versions may possibly continuously be hottest to linguistic versions. nonetheless, linguistic versions could 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
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.
13) is applied to the new winner rule again. , until Xp is correctly classified). 50 3. 13). , the current linguistic rule-based system before the modification and two modified ones) on training patterns. Then we replace the current linguistic rule-based system with the best one among the three alternatives. When all the given training patterns are correctly classified, the analytical learning scheme no longer changes the current linguistic rule-based system. Even when some training patterns are misclassified, the current linguistic rule-based system is not modified if no improvement in the classification rate can be achieved.
We used the third definition for specifying the initial rule weight of each linguistic rule. 8. From these tables, we can see that the classification performance of linguistic rule-based systems on the wine data was improved by the rule weight learning. , only three rules), the rule weight learning could not improve classification rates on training patterns as well as test patterns. On the other hand, the eff'ect of the rule weight learning was significant when the number of linguistic rules was not too small.