Sciweavers

ICMLA
2010

A New Approach to Classification with the Least Number of Features

13 years 1 months ago
A New Approach to Classification with the Least Number of Features
Recently, the so-called Support Feature Machine (SFM) was proposed as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. We propose an extension for linearly non-separable datasets that allows a direct trade-off between the number of misclassified data points and the number of dimensions. Results on toy examples as well as real-world datasets demonstrate that this method is able to identify relevant features very effectively. Keywords-Support feature machine, feature selection, zero norm minimisation, classification.
Sascha Klement, Thomas Martinetz
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICMLA
Authors Sascha Klement, Thomas Martinetz
Comments (0)