Energy Supervised Relevance Neural Gas for Feature Ranking

Authors

Angel Caţaron, Transilvania University of Brasov
Răzvan Andonie, Central Washington UniversityFollow

Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

7-10-2010

Abstract

In pattern classification, input pattern features usually contribute differently, in accordance to their relevances for a specific classification task. In a previous paper, we have introduced the Energy Supervised Relevance Neural Gas classifier, a kernel method which uses the maximization of Onicescu’s informational energy for computing the relevances of input features. Relevances were used to improve classification accuracy. In our present work, we focus on the feature ranking capability of this approach. We compare our algorithm to standard feature ranking methods.

Comments

This article was originally published in Neural Processing Letters. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Recommended Citation

Caţaron, A., & Andonie, R. (2010). Energy Supervised Relevance Neural Gas for Feature Ranking. Neural Processing Letters, 32(1), 59–73. https://doi.org/10.1007/s11063-010-9143-z

Journal

Neural Processing Letters

Rights

Copyright © 2010, Springer Science Business Media, LLC.