Missing Pattern Matching of Rough Set Based on Attribute Variations Minimization in Rough Set

Attribute Reduction Based on Approximation Set of Rough Set ⋆

Attribute reduction is one of the core issues of the rough set theory. The traditional method of attribute reduction was designed based on positive region unchanged, as abandoning processing the incompatible data in the boundary domain, so the classification characteristics of the reduction results are not necessarily best. In this paper, the concept of relative approximate degree is proposed i...

The Rough Set Attribute

A Rough Set Approach to Data with Missing Attribute Values

In this paper we discuss four kinds of missing attribute values: lost values (the values that were recorded but currently are unavailable), ”do not care” conditions (the original values were irrelevant), restricted ”do not care” conditions (similar to ordinary ”do not care” conditions but interpreted differently, these missing attribute values may occur when in the same data set there are lost ...

Three Approaches to Missing Attribute Values: A Rough Set Perspective

A new approach to missing attribute values, based on the idea of an attribute-concept value, is studied in the paper. This approach, together with two other approaches to missing attribute values, based on "do not care" conditions and lost values are discussed using rough set methodology, including attribute-value pair blocks, characteristic sets, and characteristic relations. Characteristic se...

Rough Set Strategies to Data with Missing Attribute Values

In this paper we assume that a data set is presented in the form of the incompletely specified decision table, i.e., some attribute values are missing. Our next basic assumption is that some of the missing attribute values are lost (e.g., erased) and some are "do not care" conditions (i.e., they were redundant or not necessary to make a decision or to classify a case). Incompletely specified de...