Title: Attribute reduction algorithm of enterprise credit evaluation and its applications for web-based communities

Authors: Xinhui Cui; Wang Liu; Zhiqiang He

Addresses: School of Information Engineering and Computer, HeBei Finance University, Baoding 071051, HeBei, China ' School of Information Engineering and Computer, HeBei Finance University, Baoding 071051, HeBei, China ' School of Information Engineering and Computer, HeBei Finance University, Baoding 071051, HeBei, China

Abstract: Credit becomes an import issue for community and enterprise development. The development of internet makes available of enterprise credit data, which makes it possible to research enterprise credit evaluation. In this work, we propose an attribute reduction algorithm based on semantic analysis, rough set (RS) theory and conditional entropy. The collection of topic-related words is produced on the basis of semantic analysis and it is considered as the conditional attribute set. Then attribute reduction is actualised based on RS theory and conditional entropy. The method is applied to enterprise and community credit data which is available from the web. Extensive experiments on real-word data show that our method can obtain optimal attribute efficiently.

Keywords: web-based communities; semantic analysis; rough set; information entropy; attribute reduction; community and enterprise credit evaluation.

DOI: 10.1504/IJWBC.2021.119473

International Journal of Web Based Communities, 2021 Vol.17 No.4, pp.293 - 304

Received: 10 Sep 2020
Accepted: 12 Mar 2021
Published online: 06 Dec 2021 *

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