Tuning the Fuzzy Classification Models with Various Learning Criteria: the Case of Credit Data Classification

dc.contributor.author	Shtovba, S.	en
dc.contributor.author	Pankevich, O.	en
dc.contributor.author	Dounias, G	en
dc.contributor.author	Штовба, С. Д.	uk
dc.contributor.author	Панкевич, О. Д.	uk
dc.contributor.author	Доуниас, Г.	uk
dc.date.accessioned	2020-12-21T12:25:04Z
dc.date.available	2020-12-21T12:25:04Z
dc.date.issued	2004
dc.identifier.citation	Shtovba S.Tuning the Fuzzy Classification Models with Various Learning Criteria: the Case of Credit Data Classification [Text] / S. Shtovba, O. Pankevich, G. Dounias // Proc. of Inter. Conference on Fuzzy Sets and Soft Computing in Economics and Finance, Saint-Petersburg (Russia), 17–20 June 2004. – Saint-Petersburg : Russian Fuzzy Systems Association, 2004. – Vol. 1. – P. 103–110.	en
dc.identifier.uri	http://ir.lib.vntu.edu.ua//handle/123456789/31078
dc.description.abstract	In this paper we study the efficiency of various learning criteria for the proper tuning of a fuzzy classifier. Different cases of crisp and noisy class borders are considered, and a specific credit-risk application is discussed.	en
dc.language.iso	en	en
dc.publisher	Russian Fuzzy Systems Association	en
dc.relation.ispartof	Proc. of Inter. Conference on Fuzzy Sets and Soft Computing in Economics and Finance, Saint-Petersburg (Russia), 17–20 June 2004. Vol. 1 : 103–110.	en
dc.title	Tuning the Fuzzy Classification Models with Various Learning Criteria: the Case of Credit Data Classification	en
dc.type	Article