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Chaotic Particle Swarm Optimization with Mutation for Classification

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence i...

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Autores principales: Assarzadeh, Zahra, Naghsh-Nilchi, Ahmad Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4335141/
https://www.ncbi.nlm.nih.gov/pubmed/25709937
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author Assarzadeh, Zahra
Naghsh-Nilchi, Ahmad Reza
author_facet Assarzadeh, Zahra
Naghsh-Nilchi, Ahmad Reza
author_sort Assarzadeh, Zahra
collection PubMed
description In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.
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spelling pubmed-43351412015-02-23 Chaotic Particle Swarm Optimization with Mutation for Classification Assarzadeh, Zahra Naghsh-Nilchi, Ahmad Reza J Med Signals Sens Original Article In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. Medknow Publications & Media Pvt Ltd 2015 /pmc/articles/PMC4335141/ /pubmed/25709937 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Assarzadeh, Zahra
Naghsh-Nilchi, Ahmad Reza
Chaotic Particle Swarm Optimization with Mutation for Classification
title Chaotic Particle Swarm Optimization with Mutation for Classification
title_full Chaotic Particle Swarm Optimization with Mutation for Classification
title_fullStr Chaotic Particle Swarm Optimization with Mutation for Classification
title_full_unstemmed Chaotic Particle Swarm Optimization with Mutation for Classification
title_short Chaotic Particle Swarm Optimization with Mutation for Classification
title_sort chaotic particle swarm optimization with mutation for classification
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4335141/
https://www.ncbi.nlm.nih.gov/pubmed/25709937
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