Name
Title | ||
|---|---|---|
Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis. |
Abstract | ||
|---|---|---|
One of the most powerful, popular and accurate classification techniques is support vector machines (SVMs). In this work, we want to evaluate whether the accuracy of SVMs can be further improved using training set selection (TSS), where only a subset of training instances is used to build the SVM model. By contrast to existing approaches, we focus on wrapper TSS techniques, where candidate subsets of training instances are evaluated using the SVM training accuracy. We consider five wrapper TSS strategies and show that those based on evolutionary approaches can significantly improve the accuracy of SVMs. (C) 2015 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.asoc.2015.09.006 | Applied Soft Computing |
Keywords | Field | DocType |
Support vector machines,Training set selection,Data reduction | Training set,Data mining,Interpretability,Computer science,Support vector machine,Preprocessor,Instance selection,Artificial intelligence,Machine learning | Journal |
Volume | ISSN | Citations |
38 | 1568-4946 | 7 |
PageRank | References | Authors |
0.44 | 27 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nele Verbiest | 1 | 232 | 12.23 |
| Joaquín Derrac | 2 | 2552 | 64.42 |
| Chris Cornelis | 3 | 2116 | 113.39 |
| Salvador García | 4 | 4151 | 118.45 |
| Francisco Herrera | 5 | 27391 | 1168.49 |