Computational Intelligence and Feature Selection: Rough and by Richard Jensen

By Richard Jensen

The tough and fuzzy set methods awarded right here open up many new frontiers for endured learn and improvement. Computational Intelligence and have choice presents readers with the history and primary rules at the back of characteristic choice (FS), with an emphasis on suggestions in accordance with tough and fuzzy units. For readers who're much less acquainted with the topic, the publication starts with an advent to fuzzy set concept and fuzzy-rough set idea.

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Extra resources for Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

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Otherwise, μA∩B (x) = 0, given that μA (x) and μB (x) take values only from {0, 1} in this case. 3 Simple Example An example should help with the understanding of the basic concepts and operators introduced above. Suppose that the universe of discourse, X, is a class of students and that a group, A, of students within this class are said to be tall in height. Thus A is a fuzzy subset of X (but X itself is not fuzzy), since the boundary between tall and not tall cannot be naturally defined with a fixed real number.

Hence two supplementary processes are necessary to enable the use of an FLC, which are respectively called the fuzzification and the defuzzification. Fuzzification is, however, largely a task of domain-specific data interpretation and thus is left out from the present discussion. 1 Defuzzification There exist a number of methods for defuzzification [297], though there is no clear winner. Two widely used methods are briefly summarized below. • The center of gravity method finds the geometrical center yˆ in the inferred fuzzy value D of the conclusion attribute y as the defuzzified value, such that yˆ = • yμD (y) μD (y) The mean of maxima method finds the value whose membership degree is the largest in D as the defuzzified value; if there are more than one value that has the maximum degree, then the average of them is taken as the defuzzified value.

Suppose that the universe of discourse, X, is a class of students and that a group, A, of students within this class are said to be tall in height. Thus A is a fuzzy subset of X (but X itself is not fuzzy), since the boundary between tall and not tall cannot be naturally defined with a fixed real number. 2. is much more appealing. Similarly the fuzzy term very tall can be represented by another fuzzy (sub-)set as also shown in this figure. Given such a definition of the fuzzy set A = tall, a proposition like “student x is tall” can be denoted by μA (x).

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