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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jing Zhang Xuegang Hu Peipei Li Wei He Yuhong Zhang Huizong Li |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Econ. & Manage., Anhui Univ. of Sci. & Technol., Huainan, China (Huizong Li) || Sch. of Comput. & Inf., Hefei Univ. of Technol., Hefei, China (Jing Zhang; Xuegang Hu; Peipei Li; Wei He; Yuhong Zhang) |
| Abstract | Selecting a feature subset with strong discriminative power is a critical process for high dimensional data analysis, which has attracted much attention in many application domains, such as text categorization and genome projects. Since traditional feature selection methods provide limited contributions to classification, many researchers resort to hybrid or elaborate approaches to choose interesting features. In this paper, we propose a novel hybrid approach by correlation-based filters and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method for robust feature selection, which aims to yield robust results by aggregating multiple feature subsets (groups). Specifically, in the first stage, we incorporate correlation-based filters to identify Predominant Features and Complementary Features, and generate multiple groups for robustness, in the second stage, we aggregate multiple groups with SVM-RFE into a compact feature subset for high classification accuracy. Extensive experimental studies on both UCI data sets and microarray data sets have confirmed the effectiveness of our proposed approach. |
| Starting Page | 3684 |
| Ending Page | 3689 |
| File Size | 174975 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.633 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Accuracy Support vector machines Classification algorithms Robustness Information filters Algorithm design and analysis multiple groups feature selection correlation-based filters SVM-RFE |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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