Download Classification and Modeling with Linguistic Information by Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii PDF

By Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii

While desktops can simply deal with even advanced and nonlinear mathematical types, human info processing is especially in keeping with linguistic wisdom. So the most good thing about utilizing linguistic phrases inspite of obscure levels is the intuitive interpretability of linguistic ideas. Ishibuchi and his coauthors clarify how category and modeling might be dealt with in a human-understandable demeanour. They layout a framework which could extract linguistic wisdom from numerical information through first determining linguistic phrases, then combining those phrases into linguistic principles, and at last developing a rule set from those linguistic principles. They mix their process with state of the art delicate computing innovations akin to multi-objective genetic algorithms, genetics-based computer studying, and fuzzified neural networks. ultimately they exhibit the usability of the mixed concepts with a number of simulation effects. during this mostly self-contained quantity, scholars focusing on smooth computing will relish the precise presentation, rigorously mentioned algorithms, and the numerous simulation experiments, whereas researchers will discover a wealth of recent layout schemes, thorough research, and encouraging new learn.

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Extra resources for Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing), 1st edition, 2004

Example text

We also examine the effect of the granularity of linguistic discretization on the classification performance of linguistic rule-based systems. Furthermore we compare the product operator with the minimum operator. In our computer simulations, the granularity of linguistic discretization means the number of linguistic terms in Fig. 4 of Chap. 1. First all the attribute values were normalized into real numbers in the unit interval [0,1]. This means that the iris data set was handled as a three-class pattern classification problem in the four-dimensional unit hypercube [0,1]^.

For simplicity of discussion, we assume that the unit interval [0,1] in Fig. 13 is a part of a larger entire pattern space. , Class M) exist in the other region of the pattern space. From these assumptions, we can discuss the specification of rule weights locally in the unit interval [0,1]. In this situation, the increase in the number of classes has no effect on the rule weight specification except for the second definition. 22). Thus the second definition is not the same as the third and fourth definitions when pattern classification problems involve more than two classes.

7% result in this table Rule weight definition 1st def. 2nd def. 3rd def. 4% 4th def. 9. Classification rates on test patterns in the wine data set. The leavingone-out technique was used to examine the generalization ability of linguistic rulebased classification systems. 7% = 5 result in this table Rule weight definition 1st def. 2nd def. 3rd def. 3% 4th def. 9, we used a large number of linguistic rules. From the viewpoint of interpretability, rule-based systems with only a small number of rules are desirable.

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