ScholarLens Back to search
2014•2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)Requires access

An attribute reduction algorithm in the incomplete information system based on the attribute significance

Zhen Chen, Xue Xing

Open publisher page 0 citations

Abstract

This paper proposes an attribute reduction algorithm based on attribute significance in the incomplete information system. The algorithm makes use of the concept of similar matrix via tolerance relationship. In the similar matrix, attribute significance reflects the ability of distinguishing between objects. The more frequent the appearance times are, the less importance the attribute is. The attribute reflects the higher similarity of objects. Then a new algorithm is presented which adds the attribute into the reduction set based on the attribute significance. Experiment results show that the algorithm is correct and effective.

About this research paper

What this paper is about

This paper proposes an attribute reduction algorithm based on attribute significance in the incomplete information system. The algorithm makes use of the concept of similar matrix via tolerance relationship. In the similar matrix, attribute significance reflects the ability of distinguishing between objects. The more frequent the appearance times are, the less importance the attribute is. The attribute reflects the higher similarity of objects. Then a new algorithm is presented which adds the attribute into the reduction set based on the attribute significance. Experiment results show that the algorithm is correct and effective.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

This paper proposes an attribute reduction algorithm based on attribute significance in the incomplete information system. The algorithm makes use of the concept of similar matrix via tolerance relationship. In the similar matrix, attribute significance reflects the ability of distinguishing between objects. The more frequent the appearance times are, the less importance the attribute is. The attribute reflects the higher similarity of objects. Then a new algorithm is presented which adds the attribute into the reduction set based on the attribute significance. Experiment results show that the algorithm is correct and effective.

Key concepts: Attribute domain, Reduction (mathematics), Rough set, Variable and attribute, Similarity (geometry), Computer science, Data mining, Set (abstract data type)

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
An attribute reduction algorithm in the incomplete information system based on the attribute significance — Research Paper | ScholarLens