An Improved Feature Selection Based on Effective Range for Classification
Feature selection is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier’s classification accuracy and generalization performance. Recently, a statistical feature selection method named effective range based gene selec...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/972125 |
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| _version_ | 1832556188541124608 |
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| author | Jianzhong Wang Shuang Zhou Yugen Yi Jun Kong |
| author_facet | Jianzhong Wang Shuang Zhou Yugen Yi Jun Kong |
| author_sort | Jianzhong Wang |
| collection | DOAJ |
| description | Feature selection is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier’s classification accuracy and generalization performance. Recently, a statistical feature selection method named effective range based gene selection (ERGS) is proposed. However, ERGS only considers the overlapping area (OA) among effective ranges of each class for every feature; it fails to handle the problem of the inclusion relation of effective ranges. In order to overcome this limitation, a novel efficient statistical feature selection approach called improved feature selection based on effective range (IFSER) is proposed in this paper. In IFSER, an including area (IA) is introduced to characterize the inclusion relation of effective ranges. Moreover, the samples’ proportion for each feature of every class in both OA and IA is also taken into consideration. Therefore, IFSER outperforms the original ERGS and some other state-of-the-art algorithms. Experiments on several well-known databases are performed to demonstrate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-673d3b8cd6cc4bcaa2d702ae46aa9a03 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-673d3b8cd6cc4bcaa2d702ae46aa9a032025-02-03T05:46:02ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/972125972125An Improved Feature Selection Based on Effective Range for ClassificationJianzhong Wang0Shuang Zhou1Yugen Yi2Jun Kong3College of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, ChinaCollege of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, ChinaCollege of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, ChinaCollege of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, ChinaFeature selection is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier’s classification accuracy and generalization performance. Recently, a statistical feature selection method named effective range based gene selection (ERGS) is proposed. However, ERGS only considers the overlapping area (OA) among effective ranges of each class for every feature; it fails to handle the problem of the inclusion relation of effective ranges. In order to overcome this limitation, a novel efficient statistical feature selection approach called improved feature selection based on effective range (IFSER) is proposed in this paper. In IFSER, an including area (IA) is introduced to characterize the inclusion relation of effective ranges. Moreover, the samples’ proportion for each feature of every class in both OA and IA is also taken into consideration. Therefore, IFSER outperforms the original ERGS and some other state-of-the-art algorithms. Experiments on several well-known databases are performed to demonstrate the effectiveness of the proposed method.http://dx.doi.org/10.1155/2014/972125 |
| spellingShingle | Jianzhong Wang Shuang Zhou Yugen Yi Jun Kong An Improved Feature Selection Based on Effective Range for Classification The Scientific World Journal |
| title | An Improved Feature Selection Based on Effective Range for Classification |
| title_full | An Improved Feature Selection Based on Effective Range for Classification |
| title_fullStr | An Improved Feature Selection Based on Effective Range for Classification |
| title_full_unstemmed | An Improved Feature Selection Based on Effective Range for Classification |
| title_short | An Improved Feature Selection Based on Effective Range for Classification |
| title_sort | improved feature selection based on effective range for classification |
| url | http://dx.doi.org/10.1155/2014/972125 |
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