Article ID Journal Published Year Pages File Type
394752 Information Sciences 2009 10 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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