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New classification method based on m...

Amezcua, Jonathan.

New classification method based on modular neural networks with the LVQ algorithm and type-2 fuzzy logic

Record Type:	Electronic resources : Monograph/item
Title/Author:	New classification method based on modular neural networks with the LVQ algorithm and type-2 fuzzy logicby Jonathan Amezcua, Patricia Melin, Oscar Castillo.
Author:	Amezcua, Jonathan.
other author:	Melin, Patricia.
Published:	Cham :Springer International Publishing :2018.
Description:	viii, 73 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)
Online resource:	http://dx.doi.org/10.1007/978-3-319-73773-7
ISBN:	9783319737737$q(electronic bk.)

New classification method based on modular neural networks with the LVQ algorithm and type-2 fuzzy logic
Amezcua, Jonathan.

New classification method based on modular neural networks with the LVQ algorithm and type-2 fuzzy logic[electronic resource] /by Jonathan Amezcua, Patricia Melin, Oscar Castillo. - Cham :Springer International Publishing :2018. - viii, 73 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.

ISBN: 9783319737737$q(electronic bk.)

Standard No.: 10.1007/978-3-319-73773-7doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

New classification method based on modular neural networks with the LVQ algorithm and type-2 fuzzy logic
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245 1 0 $a New classification method based on modular neural networks with the LVQ algorithm and type-2 fuzzy logic $h [electronic resource] / $c by Jonathan Amezcua, Patricia Melin, Oscar Castillo.
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520 $a In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.
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