Paper Info
Title
A Linguistic K-Nearest Prototype With An Application To Management Surveys
Abstract
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.
Year
DOI
Venue
2003
10.1109/FUZZ.2003.1206529
PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2
Keywords
Field
DocType
information analysis,pattern recognition,fuzzy sets,prototypes,fuzzy number,vectors,binary numbers,mathematical model,data sets,fuzzy set theory,fuzzy numbers,data collection,uncertainty,data analysis
Binary form,Data set,Extension principle,Computer science,Fuzzy set,Decomposition theorem,Artificial intelligence,Fuzzy number,Real number,Linguistics,Machine learning,Binary number
Conference
ISSN
Citations 
PageRank 
1098-7584
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
S. Auephanwiriyakul124639.45