Multi-Classification by Categorical Features via Clustering
2008
Talk
ei
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.
Author(s): | Seldin, Y. |
Year: | 2008 |
Month: | June |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Talk (talk) |
Digital: | 0 |
Event Name: | 25th International Conference on Machine Learning (ICML 2008) |
Event Place: | Helsinki, Finland |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PDF
Web |
BibTex @talk{6601, title = {Multi-Classification by Categorical Features via Clustering}, author = {Seldin, Y.}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = jun, year = {2008}, doi = {}, month_numeric = {6} } |