Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering
Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-11-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940231173748 |
| _version_ | 1797631099123269632 |
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| author | Zhao Zhengtian Rui Zhiyuan Duan Xiaoyan |
| author_facet | Zhao Zhengtian Rui Zhiyuan Duan Xiaoyan |
| author_sort | Zhao Zhengtian |
| collection | DOAJ |
| description | Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier. |
| first_indexed | 2024-03-11T11:17:15Z |
| format | Article |
| id | doaj.art-2b2441e3d8a44229b149d0979008011f |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-03-11T11:17:15Z |
| publishDate | 2023-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-2b2441e3d8a44229b149d0979008011f2023-11-10T19:33:19ZengSAGE PublishingMeasurement + Control0020-29402023-11-015610.1177/00202940231173748Feature selection for binary classification based on class labeling, SOM, and hierarchical clusteringZhao Zhengtian0Rui Zhiyuan1Duan Xiaoyan2College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, ChinaSchool of Mechanical and Electronic Engineering, Lanzhou University of Technology, Lanzhou, ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, ChinaFeature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier.https://doi.org/10.1177/00202940231173748 |
| spellingShingle | Zhao Zhengtian Rui Zhiyuan Duan Xiaoyan Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering Measurement + Control |
| title | Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering |
| title_full | Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering |
| title_fullStr | Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering |
| title_full_unstemmed | Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering |
| title_short | Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering |
| title_sort | feature selection for binary classification based on class labeling som and hierarchical clustering |
| url | https://doi.org/10.1177/00202940231173748 |
| work_keys_str_mv | AT zhaozhengtian featureselectionforbinaryclassificationbasedonclasslabelingsomandhierarchicalclustering AT ruizhiyuan featureselectionforbinaryclassificationbasedonclasslabelingsomandhierarchicalclustering AT duanxiaoyan featureselectionforbinaryclassificationbasedonclasslabelingsomandhierarchicalclustering |