A linguistic K-nearest prototype with an application to management surveys

For many years, one of the problems in pattern recognition is classification. There are many methods that deal with this type of problem. The data sets are sometimes in the binary form (real number) and represented by vectors of binary numbers (real numbers) although there are uncertainties in the d...

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Main Author:	Auephanwiriyakul S.
Other Authors:	Nasraoui OFrigui HKeller J.M.
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Online Access:	http://www.scopus.com/inward/record.url?eid=2-s2.0-0038198692&partnerID=40&md5=af65c571da7308acd87394615a047eca http://cmuir.cmu.ac.th/handle/6653943832/1466
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Institution:	Chiang Mai University
Language:	English

id	th-cmuir.6653943832-1466
record_format	dspace
spelling	th-cmuir.6653943832-14662014-08-29T09:29:20Z A linguistic K-nearest prototype with an application to management surveys Auephanwiriyakul S. Nasraoui OFrigui HKeller J.M. For many years, one of the problems in pattern recognition is classification. There are many methods that deal with this type of problem. The data sets are sometimes in the binary form (real number) and represented by vectors of binary numbers (real numbers) although there are uncertainties in the data, e.g., data collected in management questionnaires. In this paper, we developed a linguistic K-nearest prototype algorithm with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principle and the decomposition theorem. We apply this algorithm to linguistic vectors derived from a set of thirty-nine subjects answering questions about students' satisfaction with communication to their university. 2014-08-29T09:29:20Z 2014-08-29T09:29:20Z 2003 Conference Paper 61132 PIFSF http://www.scopus.com/inward/record.url?eid=2-s2.0-0038198692&partnerID=40&md5=af65c571da7308acd87394615a047eca http://cmuir.cmu.ac.th/handle/6653943832/1466 English
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
language	English
description	For many years, one of the problems in pattern recognition is classification. There are many methods that deal with this type of problem. The data sets are sometimes in the binary form (real number) and represented by vectors of binary numbers (real numbers) although there are uncertainties in the data, e.g., data collected in management questionnaires. In this paper, we developed a linguistic K-nearest prototype algorithm with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principle and the decomposition theorem. We apply this algorithm to linguistic vectors derived from a set of thirty-nine subjects answering questions about students' satisfaction with communication to their university.
author2	Nasraoui OFrigui HKeller J.M.
author_facet	Nasraoui OFrigui HKeller J.M. Auephanwiriyakul S.
format	Conference or Workshop Item
author	Auephanwiriyakul S.
spellingShingle	Auephanwiriyakul S. A linguistic K-nearest prototype with an application to management surveys
author_sort	Auephanwiriyakul S.
title	A linguistic K-nearest prototype with an application to management surveys
title_short	A linguistic K-nearest prototype with an application to management surveys
title_full	A linguistic K-nearest prototype with an application to management surveys
title_fullStr	A linguistic K-nearest prototype with an application to management surveys
title_full_unstemmed	A linguistic K-nearest prototype with an application to management surveys
title_sort	linguistic k-nearest prototype with an application to management surveys
publishDate	2014
url	http://www.scopus.com/inward/record.url?eid=2-s2.0-0038198692&partnerID=40&md5=af65c571da7308acd87394615a047eca http://cmuir.cmu.ac.th/handle/6653943832/1466
_version_	1681419675809873920

A linguistic K-nearest prototype with an application to management surveys

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