Can fuzzy logic be used for classification?

As we know, classification technique of data mining classifies the data into a set of classes based on some attributes for further processing. We have developed a new algorithm to handle the classification by using fuzzy rules on the real world data set.

What is the necessity of fuzzy classification process?

Fuzzy classification can reduce the dimensionality of multivariate data sets, by assigning the objects in the data set to k fuzzy classes.

What is fuzzy logic classifier?

One possible definition of a fuzzy classifier is given in (Kuncheva 2000) as ‘Any classifier that uses fuzzy sets or fuzzy logic in the course of its training or operation’.

What is fuzzy classification in remote sensing?

In a fuzzy representation for remote sensing image analysis, land-cover classes can be defined as fuzzy sets, and pixels as set elements. Each pixel is attached with a group of membership grades to indicate the extent to which the pixel belongs to certain classes.

What are fuzzy categories?

Thus is makes sense to introduce categories whose morphisms are associated with a plausibility degree that determines to what extend it is possible to “go” from one object to another one. These categories are called {\em fuzzy categories}.

What is fuzzy logic systems explain?

Fuzzy logic is a computing technique that is based on the degree of truth. A fuzzy logic system uses the input’s degree of truth and linguistic variables to produce a certain output. The state of this input determines the nature of the output. In boolean logic, two categories (0 and 1) are used to describe objects.

What is fuzzy pattern?

Classical models of pattern recognition partition a set of patterns into classes depending on the similarity in features of the patterns. The algorithm for fuzzy pattern recognition is numerically illustrated, and its application in object recognition from real time video frames is also presented.

What does ~t mean in fuzzy classification?

Here, ~T is the set of fuzzy truth values (the interval between zero and one). The fuzzy classification predicate ~Π corresponds to a fuzzy restriction “i is R” of U, where R is a fuzzy set defined by a truth function.

What is fuzzy logic in Computer Science?

Fuzzy logic. Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.

What is fuzzy classification in ABA?

A fuzzy classification corresponds to a membership function μ that indicates whether an individual is a member of a class, given its fuzzy classification predicate ~Π. μ∶~PF × U ⟶ ~T. Here, ~T is the set of fuzzy truth values (the interval between zero and one).

What is the difference between classical and fuzzy sets?

Fuzzy sets generalize classical sets, since the indicator functions (aka characteristic functions) of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1. In fuzzy set theory, classical bivalent sets are usually called crisp sets.