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Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer...

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Detalles Bibliográficos
Autores principales: Przybyła-Kasperek, Małgorzata, Marfo, Kwabena Frimpong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700412/
https://www.ncbi.nlm.nih.gov/pubmed/34945874
http://dx.doi.org/10.3390/e23121568
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author Przybyła-Kasperek, Małgorzata
Marfo, Kwabena Frimpong
author_facet Przybyła-Kasperek, Małgorzata
Marfo, Kwabena Frimpong
author_sort Przybyła-Kasperek, Małgorzata
collection PubMed
description The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object.
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spelling pubmed-87004122021-12-24 Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources Przybyła-Kasperek, Małgorzata Marfo, Kwabena Frimpong Entropy (Basel) Article The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object. MDPI 2021-11-25 /pmc/articles/PMC8700412/ /pubmed/34945874 http://dx.doi.org/10.3390/e23121568 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Przybyła-Kasperek, Małgorzata
Marfo, Kwabena Frimpong
Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources
title Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources
title_full Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources
title_fullStr Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources
title_full_unstemmed Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources
title_short Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources
title_sort neural network used for the fusion of predictions obtained by the k-nearest neighbors algorithm based on independent data sources
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700412/
https://www.ncbi.nlm.nih.gov/pubmed/34945874
http://dx.doi.org/10.3390/e23121568
work_keys_str_mv AT przybyłakasperekmałgorzata neuralnetworkusedforthefusionofpredictionsobtainedbytheknearestneighborsalgorithmbasedonindependentdatasources
AT marfokwabenafrimpong neuralnetworkusedforthefusionofpredictionsobtainedbytheknearestneighborsalgorithmbasedonindependentdatasources