Paper Info
Title
A New Approach to Classification with the Least Number of Features
Abstract
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 hyper plane. 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.
Year
DOI
Venue
2010
10.1109/ICMLA.2010.28
Machine Learning and Applications
Keywords
Field
DocType
relevant feature,novel approach,real-world datasets,hyper plane,data point,feature machine,direct trade-off,new approach,so-called support,toy example,linearly non-separable datasets,minimization,minimisation,learning artificial intelligence,machine learning,support vector machines,noise,feature selection,classification,bioinformatics
Data point,Feature selection,Pattern recognition,Computer science,Support vector machine,Minification,Minimisation (psychology),Artificial intelligence,Hyperplane,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-9211-4
2
0.46
References 
Authors
8
2
Name
Order
Citations
PageRank
Sascha Klement1243.26
Thomas Martinetz21462231.48