Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
394752 | Information Sciences | 2009 | 10 Pages |
Abstract
We introduce a novel wrapper Algorithm for Feature Selection, using Support Vector Machines with kernel functions. Our method is based on a sequential backward selection, using the number of errors in a validation subset as the measure to decide which feature to remove in each iteration. We compare our approach with other algorithms like a filter method or Recursive Feature Elimination SVM to demonstrate its effectiveness and efficiency.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Sebastián Maldonado, Richard Weber,