Quadratic Programming Feature Selection

Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named Quadratic Programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit the computational complexity of solving the optimization problem, QPFS uses the Nystr

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Irene Rodriguez-Lujan, Ramón Huerta, Charles Elkan, Carlos Santa Cruz
• Keywords: Classification Accuracy, Data Sets, JMLR 2010, Large Data Sets