A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosis
Effective and personalized treatment relies heavily on skin disease categorization. In the stratification of skin disorders, it is crucial to identify the subtypes of illnesses to provide an efficient therapy. To attain this aim, researchers have focused their attention on cluster algorithms for the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-07-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2022-0028 |
| _version_ | 1811203036804022272 |
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| author | Baireddy Ravinder Reddy Nagaraja R. |
| author_facet | Baireddy Ravinder Reddy Nagaraja R. |
| author_sort | Baireddy Ravinder Reddy |
| collection | DOAJ |
| description | Effective and personalized treatment relies heavily on skin disease categorization. In the stratification of skin disorders, it is crucial to identify the subtypes of illnesses to provide an efficient therapy. To attain this aim, researchers have focused their attention on cluster algorithms for the stratification of skin disorders in recent decades. But, cluster algorithms have real-world drawbacks, including experimental noises, a large number of dimensions, and a poor ability to comprehend. Cluster algorithms, in particular, determine the quality of clusters using a single internal evaluation operation in the majority of cases. A single internal assessment procedure is difficult to design and robust for all datasets, which is a problem. The multi-objective particle swarm obtained high sensitivity in the existing work, but it is not able to anticipate all kinds of classes. An optimized cluster distance parameter for K-means clustering is determined using a hybrid particle swarm and moth flame optimization. Multi-objective is guided by two cluster value indices, including the K-means clustering misclassification rate and neural network classification rate. Hybrid PSO will solve the multi-objective problem to identify the optimal cluster for clustering. On the dermatological dataset from the UCI repository, MATLAB R2020a will be used to evaluate the proposed method. This will be followed by an evaluation of the proposed method’s performance using the cluster evaluation indices. |
| first_indexed | 2024-04-12T02:48:02Z |
| format | Article |
| id | doaj.art-eaac045754024fe7aec6c12acaec6a75 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-12T02:48:02Z |
| publishDate | 2022-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-eaac045754024fe7aec6c12acaec6a752022-12-22T03:51:06ZengDe GruyterJournal of Intelligent Systems2191-026X2022-07-0131187689010.1515/jisys-2022-0028A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosisBaireddy Ravinder Reddy0Nagaraja R.1Department of ISE, Bangalore Institute of Technology, Bangalore, Karnataka, IndiaDepartment of ISE, Bangalore Institute of Technology, Bangalore, Karnataka, IndiaEffective and personalized treatment relies heavily on skin disease categorization. In the stratification of skin disorders, it is crucial to identify the subtypes of illnesses to provide an efficient therapy. To attain this aim, researchers have focused their attention on cluster algorithms for the stratification of skin disorders in recent decades. But, cluster algorithms have real-world drawbacks, including experimental noises, a large number of dimensions, and a poor ability to comprehend. Cluster algorithms, in particular, determine the quality of clusters using a single internal evaluation operation in the majority of cases. A single internal assessment procedure is difficult to design and robust for all datasets, which is a problem. The multi-objective particle swarm obtained high sensitivity in the existing work, but it is not able to anticipate all kinds of classes. An optimized cluster distance parameter for K-means clustering is determined using a hybrid particle swarm and moth flame optimization. Multi-objective is guided by two cluster value indices, including the K-means clustering misclassification rate and neural network classification rate. Hybrid PSO will solve the multi-objective problem to identify the optimal cluster for clustering. On the dermatological dataset from the UCI repository, MATLAB R2020a will be used to evaluate the proposed method. This will be followed by an evaluation of the proposed method’s performance using the cluster evaluation indices.https://doi.org/10.1515/jisys-2022-0028skin diseasesclusteringmulti-objectivek-meansmoth flame optimizationparticle swarm optimization |
| spellingShingle | Baireddy Ravinder Reddy Nagaraja R. A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosis Journal of Intelligent Systems skin diseases clustering multi-objective k-means moth flame optimization particle swarm optimization |
| title | A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosis |
| title_full | A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosis |
| title_fullStr | A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosis |
| title_full_unstemmed | A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosis |
| title_short | A hybrid particle swarm optimization with multi-objective clustering for dermatologic diseases diagnosis |
| title_sort | hybrid particle swarm optimization with multi objective clustering for dermatologic diseases diagnosis |
| topic | skin diseases clustering multi-objective k-means moth flame optimization particle swarm optimization |
| url | https://doi.org/10.1515/jisys-2022-0028 |
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