Intuitionistic fuzzy multigranulation rough sets

Attribute reduction is one of the most important issues in the research of rough set theory. Numerous significance measure based heuristic attribute reduction algorithms have been presented to achieve the optimal reduct. However, how to handle the situation that multiple attributes have equally largest significances is still largely unknown. In this regard, an enhancement for heuristic attribute reduction (EHAR) in rough set is proposed. In some rounds of the process of adding attributes, those that have the same largest significance are not randomly selected, but build attribute combinations and compare their significances. Then the most significant combination rather than a randomly selected single attribute is added into the reduct. With the application of EHAR, two representative heuristic attribute reduction algorithms are improved. Several experiments are used to illustrate the proposed EHAR. The experimental results show that the enhanced algorithms with EHAR have a superior performance in achieving the optimal reduct.

An enhancement for heuristic attribute reduction algorithm in rough set

The determination of real world coordinate from image coordinate has many applications in computer vision. This paper proposes the algorithm for determination of real world coordinate of a point on a plane from its image coordinate using single calibrated camera based on simple analytic geometry. Experiment has been done using the image of chessboard pattern taken from five different views. The experiment result shows that exact real world coordinate and its approximation lie on the same plane and there are no significant difference between exact real world coordinate and its approximation.

Soft computing applications and intelligent systems: Second international multi-conference on artificial intelligence technology, m-cait 2013 shah alam, august 28-29, 2013 Proceedings

In this paper, we aim to provide a rough set method to deal with a class of set covering problem called the unicost set covering problem, which is a well-known problem in binary optimization. Firstly, by constructing a Multi-Relation Granular Computing (GrC) model of a given unicost set covering problem, the problem can be equivalently converted to the knowledge reduction problem in rough set theory. Thus, various kinds of efficient knowledge reduction methods in rough set theory can be used to solve the unicost set covering problem. Secondly, a commonly used reduction algorithm based on information entropy is proposed to compute a local minimum of the unicost set covering problem. Finally, the feasibility and efficiency of the proposed algorithm is examined by an example.

A rough set method for the unicost set covering problem

In many practical situation, some of the attribute
values for an object may be interval and set-valued. This paper introduces the interval
and set-valued information systems and decision systems. According to the
semantic relation of attribute values, interval and set-valued information
systems can be classified into two categories: disjunctive (Type
1) and conjunctive (Type 2) systems. In this paper, we mainly focus on semantic
interpretation of Type 1. Then, we define a new fuzzy preference relation and
construct a fuzzy rough set model for interval and set-valued information systems.
Moreover, based on the new fuzzy preference relation, the concepts of the
significance measure of condition attributes and the relative significance
measure of condition attributes are given in interval and set-valued decision
information systems by the introduction of fuzzy positive region and the
dependency degree. And on this basis, a heuristic algorithm for calculating fuzzy
positive region reduction in interval and set-valued decision
information systems is given. Finally, we give an illustrative example to substantiate
the theoretical arguments. The results will help us to gain much more insights
into the meaning of fuzzy rough set theory. Furthermore, it has provided a new
perspective to study the attribute reduction problem in decision systems.

/pdf/attribute-reduction-in-interval-and-set-valued-decision-1n7bu9xkyl.pdf

Attribute Reduction in Interval and Set-Valued Decision Information Systems

A reduct derived from feature selection

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A reduct derived from feature selection