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Adaptive Template Reconstruction for Effective Pattern Classification

A novel instance-based algorithm for pattern classification is presented and evaluated in this paper. This new method is motivated by the challenge of pattern classifications where only limited and/or noisy training data are available. For every classification, the proposed system transforms the que...

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Autores principales: Yang, Su, Hoque, Sanaul, Deravi, Farzin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422356/
https://www.ncbi.nlm.nih.gov/pubmed/37571491
http://dx.doi.org/10.3390/s23156707
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author Yang, Su
Hoque, Sanaul
Deravi, Farzin
author_facet Yang, Su
Hoque, Sanaul
Deravi, Farzin
author_sort Yang, Su
collection PubMed
description A novel instance-based algorithm for pattern classification is presented and evaluated in this paper. This new method is motivated by the challenge of pattern classifications where only limited and/or noisy training data are available. For every classification, the proposed system transforms the query data and the training templates based on their distributions in the feature space. One of the major novelties of the proposed method is the concept of template reconstruction enabling improved performance with limited training data. The technique is compared with similar algorithms and evaluated using both the image and time-series modalities to demonstrate its effectiveness and versatility. Two public image databases, FASHION-MNIST and CIFAR-10, were used to test its effectiveness for the classification of images using small amounts of training samples. An average classification improvement of 2~3% was observed while using a small subset of the training database, compared to the performances achieved by state-of-the-art techniques using the full datasets. To further explore its capability in solving more challenging classification problems such as non-stationary time-series electroencephalography (EEG) signals, a clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database, obtained using a low-cost system equipped with a single dry sensor, have also been used to test the algorithm. Adaptive reconstruction of the feature instances has been seen to have substantially improved class separation and matching performance for both still images and time-series signals. In particular, the method is found to be effective for the classification of noisy non-stationary data with limited training data volumes, indicating its potential suitability for a wide range of applications.
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spelling pubmed-104223562023-08-13 Adaptive Template Reconstruction for Effective Pattern Classification Yang, Su Hoque, Sanaul Deravi, Farzin Sensors (Basel) Article A novel instance-based algorithm for pattern classification is presented and evaluated in this paper. This new method is motivated by the challenge of pattern classifications where only limited and/or noisy training data are available. For every classification, the proposed system transforms the query data and the training templates based on their distributions in the feature space. One of the major novelties of the proposed method is the concept of template reconstruction enabling improved performance with limited training data. The technique is compared with similar algorithms and evaluated using both the image and time-series modalities to demonstrate its effectiveness and versatility. Two public image databases, FASHION-MNIST and CIFAR-10, were used to test its effectiveness for the classification of images using small amounts of training samples. An average classification improvement of 2~3% was observed while using a small subset of the training database, compared to the performances achieved by state-of-the-art techniques using the full datasets. To further explore its capability in solving more challenging classification problems such as non-stationary time-series electroencephalography (EEG) signals, a clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database, obtained using a low-cost system equipped with a single dry sensor, have also been used to test the algorithm. Adaptive reconstruction of the feature instances has been seen to have substantially improved class separation and matching performance for both still images and time-series signals. In particular, the method is found to be effective for the classification of noisy non-stationary data with limited training data volumes, indicating its potential suitability for a wide range of applications. MDPI 2023-07-26 /pmc/articles/PMC10422356/ /pubmed/37571491 http://dx.doi.org/10.3390/s23156707 Text en © 2023 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
Yang, Su
Hoque, Sanaul
Deravi, Farzin
Adaptive Template Reconstruction for Effective Pattern Classification
title Adaptive Template Reconstruction for Effective Pattern Classification
title_full Adaptive Template Reconstruction for Effective Pattern Classification
title_fullStr Adaptive Template Reconstruction for Effective Pattern Classification
title_full_unstemmed Adaptive Template Reconstruction for Effective Pattern Classification
title_short Adaptive Template Reconstruction for Effective Pattern Classification
title_sort adaptive template reconstruction for effective pattern classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422356/
https://www.ncbi.nlm.nih.gov/pubmed/37571491
http://dx.doi.org/10.3390/s23156707
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AT hoquesanaul adaptivetemplatereconstructionforeffectivepatternclassification
AT deravifarzin adaptivetemplatereconstructionforeffectivepatternclassification