de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Multi-Classification by Categorical Features via Clustering

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84206

Seldin,  Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Seldin, Y. (2008). Multi-Classification by Categorical Features via Clustering. In 25th International Conference on Machine Learning (ICML 2008) (pp. 920-927).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C8FF-7
Abstract
We derive a generalization bound for multi-classification schemes based on grid clustering in categorical parameter product spaces. Grid clustering partitions the parameter space in the form of a Cartesian product of partitions for each of the parameters. The derived bound provides a means to evaluate clustering solutions in terms of the generalization power of a built-on classifier. For classification based on a single feature the bound serves to find a globally optimal classification rule. Comparison of the generalization power of individual features can then be used for feature ranking. Our experiments show that in this role the bound is much more precise than mutual information or normalized correlation indices.