What is rough set theory in soft computing?

What is rough set theory in soft computing?

From Wikipedia, the free encyclopedia. In computer science, a rough set, first described by Polish computer scientist Zdzisław I. Pawlak, is a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set.

What is rough set approach?

Rough Set Approach The Rough Set Theory is based on the establishment of equivalence classes within the given training data. The tuples that forms the equivalence class are indiscernible. It means the samples are identical with respect to the attributes describing the data.

What is rough set based feature selection?

One of the applications of Rough set theory in machine learning is the so-called feature selection especially for classification problems. This is performed by means of finding a reduct set of attributes. Reduct set is a subset of all features which retains classification accuracy as original attributes.

What is Indiscernibility relation?

1. A relation which identifies a set of data. In other words the data are supposed to be similar with respect to this relation.

What is rough set classification?

Rough set theory can be used for classification to discover structural relationships within imprecise or noisy data. It applies to discrete-valued attributes. A rough set definition for a given class, C, is approximated by two sets—a lower approximation of C and an upper approximation of C.

What do you mean by fuzzy set?

A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one.

What are the goals of Soft Computing?

Main Goal of Soft Computing is to develop intelligent machines to provide solutions of real-world problems, which are not modeled or difficult to model mathematically.

What is fuzzy logic in data mining?

Fuzzy Logic is defined as a many-valued logic form which may have truth values of variables in any real number between 0 and 1. Fuzzy logic algorithm helps to solve a problem after considering all available data. Then it takes the best possible decision for the given the input.

What is decision tree in data mining?

A decision tree is a class discriminator that recursively partitions the training set until each partition consists entirely or dominantly of examples from one class. Each non-leaf node of the tree contains a split point that is a test on one or more attributes and determines how the data is partitioned.

What is fuzzy number example?

A fuzzy number is a generalization of a regular, real number in the sense that it does not refer to one single value but rather to a connected set of possible values, where each possible value has its own weight between 0 and 1. A fuzzy number is thus a special case of a convex, normalized fuzzy set of the real line.

What is the difference between fuzzy logic and fuzzy set?

Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy).

What is soft in soft computing?

Soft computing is the reverse of hard (conventional) computing. It refers to a group of computational techniques that are based on artificial intelligence (AI) and natural selection. It provides cost-effective solutions to the complex real-life problems for which hard computing solution does not exist.

What is rough set theory in Computer Science?

It is a formal theory derived from fundamental research on logical properties of information systems. Rough set theory has been a methodology of database mining or knowledge discovery in relational databases. In its abstract form, it is a new area of uncertainty mathematics closely related to fuzzy theory.

What is the difference between rough set and fuzzy set?

We can use rough set approach to discover structural relationship within imprecise and noisy data. Rough sets and fuzzy sets are complementary generalizations of classical sets. The approximation spaces of rough set theory are sets with multiple memberships, while fuzzy sets are concerned with partial memberships.

What are some advanced soft computing techniques for machine learning?

Generation cycle & convergence of GA, application areas of GA. Advanced soft computing techniques: Rough Set Theory – Introduction, Set approximation, Rough membership, Attributes, optimization. SVM – Introduction, obtaining the optimal hyper plane, linear and nonlinear SVM classifiers.

What is rough set theory in DBMS?

Rough set theory has been a methodology of database mining or knowledge discovery in relational databases. In its abstract form, it is a new area of uncertainty mathematics closely related to fuzzy theory. We can use rough set approach to discover structural relationship within imprecise and noisy data.