Feature Selection via Concave Minimization and Support Vector Machines

December 23, 2014

Computational comparison is made between two feature selection approaches for fi nding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave minimization approach [19, 5] a separating plane is generated by minimizing a weighted sum of distances of misclassified points to two parallel planes that bound the sets and which determine the separating plane midway between them.

Click here to view and download the full-screen version >>

Stay Informed

Stay Informed