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.

• Type: Journal
• Year: 2010
• Where: ICMLA
• Authors: Sascha Klement, Thomas Martinetz
• Keywords: ICMLA 2010, Machine Learning, Norm Minimisation, Separating Hyperplane, Zero Norm