Conference proceeding
Speed up SVM-RFE Procedure Using Margin Distribution
2005 IEEE Workshop on Machine Learning for Signal Processing, pp 297-302
2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper, a new method is introduced to speed up the recursive feature ranking procedure by using the margin distribution of a trained SVM. The method, M-RFE, continuously eliminates features without retraining the SVM as long as the margin distribution of the SVM does not change significantly. Synthetic datasets and two benchmark microarray datasets were tested on M-RFE. Comparison with original SVM-RFE shows that our method speeds up the feature ranking procedure considerably with little or no performance degradation. Comparison of M-RFE to a similar speed up technique, E-RFE, provides similar classification performance, but with reduced complexity
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Details
- Title
- Speed up SVM-RFE Procedure Using Margin Distribution
- Creators
- Yingqin Yuan - WhirlpoolL Hrebien - Drexel UniversityM Kam - Drexel UniversityIEEE
- Publication Details
- 2005 IEEE Workshop on Machine Learning for Signal Processing, pp 297-302
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000234650300048
- Scopus ID
- 2-s2.0-33749069293
- Other Identifier
- 991019173439804721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology
- Statistics & Probability