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...
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MDPI AG
2023-07-01
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| author | M. Priyadharshini A. Faritha Banu Bhisham Sharma Subrata Chowdhury Khaled Rabie Thokozani Shongwe |
| author_facet | M. Priyadharshini A. Faritha Banu Bhisham Sharma Subrata Chowdhury Khaled Rabie Thokozani Shongwe |
| author_sort | M. Priyadharshini |
| collection | DOAJ |
| 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. |
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| last_indexed | 2024-03-11T00:16:22Z |
| publishDate | 2023-07-01 |
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| series | Sensors |
| spelling | doaj.art-50557a5ec9ab493bb345c8d26d08840b2023-11-18T23:35:07ZengMDPI AGSensors1424-82202023-07-012315683610.3390/s23156836Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble LearningM. Priyadharshini0A. Faritha Banu1Bhisham Sharma2Subrata Chowdhury3Khaled Rabie4Thokozani Shongwe5Department of Computer Science Engineering, Nalla Malla Reddy Engineering College, Hyderabad 500088, Telangana, IndiaDepartment of Computer Science, Karpagam Academy of Higher Education, Coimbatore 631027, Tamil Nadu, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaDepartment of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, Chittoor 517127, Andra Pradesh, IndiaDepartment of Engineering, Manchester Metropolitan University, Manchester M15GD, UKDepartment of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, Johannesburg 2006, South AfricaIn 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.https://www.mdpi.com/1424-8220/23/15/6836imbalanced dataadaptive synthetic dataimproved particle swarm optimizationadaptive neuro-fuzzy inference systemprobabilistic neural networkmulti-class classification |
| spellingShingle | M. Priyadharshini A. Faritha Banu Bhisham Sharma Subrata Chowdhury Khaled Rabie Thokozani Shongwe Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning Sensors imbalanced data adaptive synthetic data improved particle swarm optimization adaptive neuro-fuzzy inference system probabilistic neural network multi-class classification |
| 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 | imbalanced data adaptive synthetic data improved particle swarm optimization adaptive neuro-fuzzy inference system probabilistic neural network multi-class classification |
| url | https://www.mdpi.com/1424-8220/23/15/6836 |
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