A novel learning vector quantization inference classifier

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:
Computer Science
Mathematics
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018582024&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/57148
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
id th-cmuir.6653943832-57148
record_format dspace
spelling th-cmuir.6653943832-571482018-09-05T03:45:18Z A novel learning vector quantization inference classifier Chakkraphop Maisen Sansanee Auephanwiriyakul Nipon Theera-Umpon Computer Science Mathematics © 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. 2018-09-05T03:35:30Z 2018-09-05T03:35:30Z 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/6653943832/57148
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Mathematics
spellingShingle Computer Science
Mathematics
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/6653943832/57148
_version_ 1681424824627363840

Similar Items