Energy Supervised Relevance Neural Gas for Feature Ranking

Authors:
Angel Caţaron;Răzvan Andonie
Affiliations:
Electronics and Computers Department, Transylvania University of Brasov, Brasov, Romania;Electronics and Computers Department, Transylvania University of Brasov, Brasov, Romania and Department of Computer Science, Central Washington University, Ellensburg, USA
Venue:
Neural Processing Letters
Year:
2010

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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.