A novel learning vector quantization inference classifier

© Springer International Publishing AG 2017. One of the popular tools in pattern recognition is a neuro-fuzzy system. Most of the neuro-fuzzy systems are based on a multi-layer perceptrons. In this paper, we incorporate learning vector quantization in a neuro-fuzzy system. The prototype update equat...

Full description

Saved in:

Bibliographic Details
Main Authors:	Chakkraphop Maisen, Sansanee Auephanwiriyakul, Nipon Theera-Umpon
Format:	Book Series
Published:	2018
Subjects:	Mathematics Agricultural and Biological Sciences
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018582024&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/46722
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Chiang Mai University

id	th-cmuir.6653943832-46722
record_format	dspace
spelling	th-cmuir.6653943832-467222018-04-25T07:28:53Z A novel learning vector quantization inference classifier Chakkraphop Maisen Sansanee Auephanwiriyakul Nipon Theera-Umpon Mathematics Agricultural and Biological Sciences © Springer International Publishing AG 2017. One of the popular tools in pattern recognition is a neuro-fuzzy system. Most of the neuro-fuzzy systems are based on a multi-layer perceptrons. In this paper, we incorporate learning vector quantization in a neuro-fuzzy system. The prototype update equation is based on the learning vector quantization while the gradient descent technique is used in the weight update equation. Since weights contain informative information, they are exploited to select a good feature set. There are 8 data sets used in the experiment, i.e., Iris Plants, Wisconsin Breast Cancer (WBC), Pima Indians Diabetes, Wine, Ionosphere, Colon Tumor, Diffuse Large B-Cell Lymphoma (DLBCL), and Glioma Tumor (GLI_85). The results show that our algorithm provides good classification rates on all data sets. It is able to select a good feature set with a small number of features. We compare our results indirectly with the existing algorithms as well. The comparison result shows that our algorithm performs better than those existing ones. 2018-04-25T06:59:51Z 2018-04-25T06:59:51Z 2017-01-01 Book Series 16113349 03029743 2-s2.0-85018582024 10.1007/978-3-319-54472-4_50 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018582024&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/46722
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Mathematics Agricultural and Biological Sciences
spellingShingle	Mathematics Agricultural and Biological Sciences Chakkraphop Maisen Sansanee Auephanwiriyakul Nipon Theera-Umpon A novel learning vector quantization inference classifier
description	© Springer International Publishing AG 2017. One of the popular tools in pattern recognition is a neuro-fuzzy system. Most of the neuro-fuzzy systems are based on a multi-layer perceptrons. In this paper, we incorporate learning vector quantization in a neuro-fuzzy system. The prototype update equation is based on the learning vector quantization while the gradient descent technique is used in the weight update equation. Since weights contain informative information, they are exploited to select a good feature set. There are 8 data sets used in the experiment, i.e., Iris Plants, Wisconsin Breast Cancer (WBC), Pima Indians Diabetes, Wine, Ionosphere, Colon Tumor, Diffuse Large B-Cell Lymphoma (DLBCL), and Glioma Tumor (GLI_85). The results show that our algorithm provides good classification rates on all data sets. It is able to select a good feature set with a small number of features. We compare our results indirectly with the existing algorithms as well. The comparison result shows that our algorithm performs better than those existing ones.
format	Book Series
author	Chakkraphop Maisen Sansanee Auephanwiriyakul Nipon Theera-Umpon
author_facet	Chakkraphop Maisen Sansanee Auephanwiriyakul Nipon Theera-Umpon
author_sort	Chakkraphop Maisen
title	A novel learning vector quantization inference classifier
title_short	A novel learning vector quantization inference classifier
title_full	A novel learning vector quantization inference classifier
title_fullStr	A novel learning vector quantization inference classifier
title_full_unstemmed	A novel learning vector quantization inference classifier
title_sort	novel learning vector quantization inference classifier
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018582024&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/46722
_version_	1681422927071805440

A novel learning vector quantization inference classifier

Similar Items