<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Traditional rough set theory is mainly used to reduce attributes and extract rules in databases in which attributes are characterised by partitions, which the covering rough set theory, a generalisation of traditional rough set theory, covers. In this article, we posit a method to reduce the attributes of covering decision systems, which are databases incarnated in the form of covers. First, we define different covering decision systems and their attributes’ reductions. Further, we describe the necessity and sufficiency for reductions. Thereafter, we construct a discernible matrix to design algorithms that compute all the reductions of covering decision systems. Finally, the above methods are illustrated using a practical example and the obtained results are contrasted with other results.</p>
</abstract>

Traditional rough set theory is mainly used to reduce attributes and extract rules in databases in which attributes are characterised by partitions, which the covering rough set theory, a generalisation of traditional rough set theory, covers. In this article, we posit a method to reduce the attributes of covering decision systems, which are databases incarnated in the form of covers. First, we define different covering decision systems and their attributes’ reductions. Further, we describe the necessity and sufficiency for reductions. Thereafter, we construct a discernible matrix to design algorithms that compute all the reductions of covering decision systems. Finally, the above methods are illustrated using a practical example and the obtained results are contrasted with other results.

Innovations to Attribute Reduction of Covering Decision System Based on Conditional Information Entropy

Hunan University of Information Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Traditional rough set theory is mainly used to reduce attributes and extract rules in databases in which attributes are characterised by partitions, which the...

Innovations to Attribute Reduction of Covering Decision System Based on Conditional Information Entropy

Online veröffentlicht: 15. Apr. 2022

Seitenbereich: 2103 - 2116

Eingereicht: 28. Okt. 2020

Akzeptiert: 26. Nov. 2020

DOI: https://doi.org/10.2478/amns.2021.1.00110

Schlüsselwörter
discernible matrix, information entropy, decision system, attribute

© 2023 Xiuyun Xia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Innovations to Attribute Reduction of Covering Decision System Based on Conditional Information Entropy

Online veröffentlicht: 15. Apr. 2022

Seitenbereich: 2103 - 2116

Eingereicht: 28. Okt. 2020

Akzeptiert: 26. Nov. 2020

DOI: https://doi.org/10.2478/amns.2021.1.00110

Schlüsselwörterdiscernible matrix, information entropy, decision system, attribute

© 2023 Xiuyun Xia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
discernible matrix, information entropy, decision system, attribute