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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pranckeviciene, E. Somorjai, R. Tran, M.N. |
| Copyright Year | 2007 |
| Description | Author affiliation: Nat. Res. Council Canada, Winnipeg (Pranckeviciene, E.; Somorjai, R.) |
| Abstract | Many real-world classification problems are represented by very sparse and high-dimensional data. The recent successes of a linear programming support vector machine (LPSVM) for feature selection motivated a deeper analysis of the method when applied to sparse, multivariate data. Due to the sparseness, the selection of a classification model is greatly influenced by the characteristics of that particular dataset. In this study, we investigate a feature selection strategy based on LPSVM as the initial feature filter, combined with state-of-art classification rules, and apply to five real-life datasets of the agnostic learning vs. prior knowledge challenge of IJCNN2007. Our goal is to better understand the robustness of LPSVM as a feature filter. Our analysis suggests that LPSVM can be a useful black box method for identification of the profile of the informative features in the data. If the data are complex and better separable by nonlinear methods, then feature pre-filtering by LPSVM enhances the data representation for other classifiers. |
| Starting Page | 1627 |
| Ending Page | 1632 |
| File Size | 1087165 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4371201 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Linear programming Support vector machines Support vector machine classification Robustness Filters Testing Error analysis Neural networks Throughput Data analysis |
| Content Type | Text |
| Resource Type | Article |
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