Attribute significance, consistency measure and attribute reduction on formal concept analysis

Title:

Attribute significance, consistency measure and attribute reduction on formal concept analysis

Creator:

Huang, C., Li., J., and Dias, S.M.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:4c399ea2-7308-44d7-af09-65bf71d4f713
uuid:4c399ea2-7308-44d7-af09-65bf71d4f713
doi:10.14311/NNW.2016.26.035

Subject:

formal concept analysis, information entropy, attribute significance, and consistency

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

One focus of data analysis in formal concept analysis is attribute-significance measure, and another is attribute reduction. From the perspective of information granules, we propose information entropy in formal contexts and conditional information entropy in formal decision contexts, and they are further used to measure attribute significance. Moreover, an approach is presented to measure the consistency of a formal decision context in preparation for calculating reducts. Finally, heuristic ideas are integrated with reduction technique to achieve the task of calculating reducts of an inconsistent data set.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:6

Harvested from:

CDK

Metadata only:

false