Search

Given any modeling problem, variable selection is a preprocess step that selects the most relevant variables with respect to the output variable. Forward selection is the most straightforward strategy for variable selection; its application using the mutual information is simple, intuitive and effective, and is commonly used in the machine learning literature. However the problem of when to stop the forward process doesn΄t have a direct satisfactory solution due to the inaccuracies of the Mutual Information estimation, specially as the number of variables considered increases. This work proposes a modified stopping criterion for this variable selection methodology that uses the Markov blanket concept. As it will be shown, this approach can increase the performance and applicability of the stopping criterion of a forward selection process using mutual information. [ABSTRACT FROM AUTHOR]

There are many papers in the literature that deal with the problem of the design of a fuzzy system from a set of given training examples. Those who get the best approximation accuracy are based on TSK fuzzy rules, which have the problem of not being as interpretable as Mamdany-type Fuzzy Systems. A question now is posed: How can the interpretability of the generated fuzzy rule-table base be increased? A possible response is to try to reduce the rule-base size by generalizing fuzzy-rules consequents which are symbolic functions instead of fixed scalar values or polynomials, and apply symbolic interpolations techniques in fuzzy system generation. A first approximation to this idea is presented in this paper for 1-D functions. [ABSTRACT FROM AUTHOR]

There are many papers in the literature that deal with the problem of the design of a fuzzy system from a set of given training examples. Those who get the best approximation accuracy are based on TSK fuzzy rules, which have the problem of not being as interpretable as Mamdany-type Fuzzy Systems. A question now is posed: How can the interpretability of the generated fuzzy rule-table base be increased? A possible response is to try to reduce the rule-base size by generalizing fuzzy-rules consequents which are symbolic functions instead of fixed scalar values or polynomials, and apply symbolic regressions technics in fuzzy system generation. A first approximation to this idea is presented in this paper for 1-D and 2D functions. [ABSTRACT FROM AUTHOR]