Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm
Too many input features in applications may lead to over-fitting and reduce the performance of the learning algorithm. Moreover, in most cases, each feature containing different information content has different effects on the prediction target. Therefore, a feature selection method for calculating...
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9151875/ |
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doaj-22f37717c0b343b186197a03203f02712021-03-30T04:37:53ZengIEEEIEEE Access2169-35362020-01-01813951213952810.1109/ACCESS.2020.30127689151875Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic AlgorithmShuangjie Li0https://orcid.org/0000-0001-6667-8330Kaixiang Zhang1Qianru Chen2Shuqin Wang3Shaoqiang Zhang4https://orcid.org/0000-0002-4127-0539College of Computer and Information Engineering, Tianjin Normal University, Tianjin, ChinaCollege of Computer and Information Engineering, Tianjin Normal University, Tianjin, ChinaCollege of Computer and Information Engineering, Tianjin Normal University, Tianjin, ChinaCollege of Computer and Information Engineering, Tianjin Normal University, Tianjin, ChinaCollege of Computer and Information Engineering, Tianjin Normal University, Tianjin, ChinaToo many input features in applications may lead to over-fitting and reduce the performance of the learning algorithm. Moreover, in most cases, each feature containing different information content has different effects on the prediction target. Therefore, a feature selection method for calculating the importance of each feature, called WKNNGAFS, is proposed in this paper. In this method, the genetic algorithm (GA) is adopted to search the optimal weight vector, the value of the ith component of which corresponds to the contribution degree of the ith feature to the classification from a global perspective. Besides, weighted K-nearest neighbors algorithm (WKNN), which takes both the different contributions of nearest neighbors and the different classification ability of each feature into account, is used to determine the target label. To evaluate the effectiveness of the proposed method, nine existing feature selection methods are compared with it on 13 real datasets, including 6 high dimensional microarray datasets. Experimental results demonstrate the method is more effective and can improve classification performance.https://ieeexplore.ieee.org/document/9151875/Feature selectionweighted K-nearest neighborsgenetic algorithmreal coding |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Shuangjie Li Kaixiang Zhang Qianru Chen Shuqin Wang Shaoqiang Zhang |
| spellingShingle |
Shuangjie Li Kaixiang Zhang Qianru Chen Shuqin Wang Shaoqiang Zhang Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm IEEE Access Feature selection weighted K-nearest neighbors genetic algorithm real coding |
| author_facet |
Shuangjie Li Kaixiang Zhang Qianru Chen Shuqin Wang Shaoqiang Zhang |
| author_sort |
Shuangjie Li |
| title |
Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm |
| title_short |
Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm |
| title_full |
Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm |
| title_fullStr |
Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm |
| title_full_unstemmed |
Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm |
| title_sort |
feature selection for high dimensional data using weighted k-nearest neighbors and genetic algorithm |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2020-01-01 |
| description |
Too many input features in applications may lead to over-fitting and reduce the performance of the learning algorithm. Moreover, in most cases, each feature containing different information content has different effects on the prediction target. Therefore, a feature selection method for calculating the importance of each feature, called WKNNGAFS, is proposed in this paper. In this method, the genetic algorithm (GA) is adopted to search the optimal weight vector, the value of the ith component of which corresponds to the contribution degree of the ith feature to the classification from a global perspective. Besides, weighted K-nearest neighbors algorithm (WKNN), which takes both the different contributions of nearest neighbors and the different classification ability of each feature into account, is used to determine the target label. To evaluate the effectiveness of the proposed method, nine existing feature selection methods are compared with it on 13 real datasets, including 6 high dimensional microarray datasets. Experimental results demonstrate the method is more effective and can improve classification performance. |
| topic |
Feature selection weighted K-nearest neighbors genetic algorithm real coding |
| url |
https://ieeexplore.ieee.org/document/9151875/ |
| work_keys_str_mv |
AT shuangjieli featureselectionforhighdimensionaldatausingweightedknearestneighborsandgeneticalgorithm AT kaixiangzhang featureselectionforhighdimensionaldatausingweightedknearestneighborsandgeneticalgorithm AT qianruchen featureselectionforhighdimensionaldatausingweightedknearestneighborsandgeneticalgorithm AT shuqinwang featureselectionforhighdimensionaldatausingweightedknearestneighborsandgeneticalgorithm AT shaoqiangzhang featureselectionforhighdimensionaldatausingweightedknearestneighborsandgeneticalgorithm |
| _version_ |
1724181409040433152 |