Cargando…

Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As...

Descripción completa

Detalles Bibliográficos
Autores principales: Priyadharshini, M., Banu, A. Faritha, Sharma, Bhisham, Chowdhury, Subrata, Rabie, Khaled, Shongwe, Thokozani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422387/
https://www.ncbi.nlm.nih.gov/pubmed/37571619
http://dx.doi.org/10.3390/s23156836
_version_ 1785089197150830592
author Priyadharshini, M.
Banu, A. Faritha
Sharma, Bhisham
Chowdhury, Subrata
Rabie, Khaled
Shongwe, Thokozani
author_facet Priyadharshini, M.
Banu, A. Faritha
Sharma, Bhisham
Chowdhury, Subrata
Rabie, Khaled
Shongwe, Thokozani
author_sort Priyadharshini, M.
collection PubMed
description In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable’s impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model’s multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.
format Online
Article
Text
id pubmed-10422387
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104223872023-08-13 Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning Priyadharshini, M. Banu, A. Faritha Sharma, Bhisham Chowdhury, Subrata Rabie, Khaled Shongwe, Thokozani Sensors (Basel) Article In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable’s impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model’s multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively. MDPI 2023-07-31 /pmc/articles/PMC10422387/ /pubmed/37571619 http://dx.doi.org/10.3390/s23156836 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
Priyadharshini, M.
Banu, A. Faritha
Sharma, Bhisham
Chowdhury, Subrata
Rabie, Khaled
Shongwe, Thokozani
Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning
title Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning
title_full Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning
title_fullStr Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning
title_full_unstemmed Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning
title_short Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning
title_sort hybrid multi-label classification model for medical applications based on adaptive synthetic data and ensemble learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422387/
https://www.ncbi.nlm.nih.gov/pubmed/37571619
http://dx.doi.org/10.3390/s23156836
work_keys_str_mv AT priyadharshinim hybridmultilabelclassificationmodelformedicalapplicationsbasedonadaptivesyntheticdataandensemblelearning
AT banuafaritha hybridmultilabelclassificationmodelformedicalapplicationsbasedonadaptivesyntheticdataandensemblelearning
AT sharmabhisham hybridmultilabelclassificationmodelformedicalapplicationsbasedonadaptivesyntheticdataandensemblelearning
AT chowdhurysubrata hybridmultilabelclassificationmodelformedicalapplicationsbasedonadaptivesyntheticdataandensemblelearning
AT rabiekhaled hybridmultilabelclassificationmodelformedicalapplicationsbasedonadaptivesyntheticdataandensemblelearning
AT shongwethokozani hybridmultilabelclassificationmodelformedicalapplicationsbasedonadaptivesyntheticdataandensemblelearning