Fuzzy classification

Fuzzy classification is the process of grouping elements into fuzzy sets ^[1] whose membership functions are defined by the truth value of a fuzzy propositional function. ^{[2] [3] [4]} A fuzzy propositional function is analogous to ^[5] an expression containing one or more variables, such that when values are assigned to these variables, the expression becomes a fuzzy proposition. ^[6]

Accordingly, fuzzy classification is the process of grouping individuals having the same characteristics into a fuzzy set. A fuzzy classification corresponds to a membership function ${\textstyle \mu _{\tilde {C}}:{\tilde {PF}}\times U\to {\tilde {T}}}$ that indicates the degree to which an individual ${\textstyle i\in U}$ is a member of the fuzzy class ${\textstyle {\tilde {C}}}$, given its fuzzy classification predicate ${\textstyle {\tilde {\Pi }}_{\tilde {C}}\in {\tilde {PF}}}$. Here, ${\textstyle {\tilde {T}}}$ is the set of fuzzy truth values, i.e., the unit interval ${\textstyle [0,1]}$. The fuzzy classification predicate ${\textstyle {\tilde {\Pi }}_{\tilde {C}}(i)}$ corresponds to the fuzzy restriction "${\textstyle i}$ is a member of ${\textstyle {\tilde {C}}}$". ^[6]

Classification

Intuitively, a class is a set that is defined by a certain property, and all objects having that property are elements of that class. The process of classification evaluates for a given set of objects whether they fulfill the classification property, and consequentially are a member of the corresponding class. However, this intuitive concept has some logical subtleties that need clarification.

A class logic ^[7] is a logical system which supports set construction using logical predicates with the class operator { .| .}. A class

C = { i | Π(i) }

is defined as a set C of individuals i satisfying a classification predicate Π which is a propositional function. The domain of the class operator { .| .} is the set of variables V and the set of propositional functions PF, and the range is the powerset of this universe P(U) that is, the set of possible subsets:

{ .| .} :V×PF⟶P(U)

Here is an explanation of the logical elements that constitute this definition:

• An individual is a real object of reference.
• A universe of discourse is the set of all possible individuals considered.
• A variable V:⟶R is a function which maps into a predefined range R without any given function arguments: a zero-place function.
• A propositional function is "an expression containing one or more undetermined constituents, such that, when values are assigned to these constituents, the expression becomes a proposition". ^[5]

In contrast, classification is the process of grouping individuals having the same characteristics into a set. A classification corresponds to a membership function μ that indicates whether an individual is a member of a class, given its classification predicate Π.

μ:PF × U ⟶ T

The membership function maps from the set of propositional functions PF and the universe of discourse U into the set of truth values T. The membership μ of individual i in Class C is defined by the truth value τ of the classification predicate Π.

μC(i):=τ(Π(i))

In classical logic the truth values are certain. Therefore a classification is crisp, since the truth values are either exactly true or exactly false.

See also

• Fuzzy logic

References

1. Zadeh, L. A. (1965). Fuzzy sets. Information and Control (8), pp. 338–353.
2. Zimmermann, H.-J. (2000). Practical Applications of Fuzzy Technologies. Springer.
3. Meier, A., Schindler, G., & Werro, N. (2008). Fuzzy classification on relational databases. In M. Galindo (Hrsg.), Handbook of research on fuzzy information processing in databases (Bd. II, S. 586-614). Information Science Reference.
4. Del Amo, A., Montero, J., & Cutello, V. (1999). On the principles of fuzzy classification. Proc. 18th North American Fuzzy Information Processing Society Annual Conf, (S. 675 – 679).
5. Russel, B. (1919). Introduction to Mathematical Philosophy. London: George Allen & Unwin, Ltd., S. 155
6. Zadeh, L. A. (1975). Calculus of fuzzy restrictions. In L. A. Zadeh, K.-S. Fu, K. Tanaka, & M. Shimura (Hrsg.), Fuzzy sets and Their Applications to Cognitive and Decision Processes. New York: Academic Press.
7. Glubrecht, J.-M., Oberschelp, A., & Todt, G. (1983). Klassenlogik. Mannheim/Wien/Zürich: Wissenschaftsverlag.