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An Informational Energy Approach to Feature Selection

Summary: An Informational Energy Approach to
Feature Selection
A. Caaron* and R. Andonie**
* Transilvania University of Brasov, Romania
** Central Washington University, USA
Abstract In this work, we focus on machine learning
methods for handling data sets containing large amounts of
irrelevant information. We address two key issues: the
problem of selecting relevant features, and the problem of
weighting (ranking) these features. We describe our Energy
Supervised Relevance Neural Gas (ESRNG) algorithm, a
kernel method which uses the maximization of Onicescu's
informational energy as a criteria to compute the relevance
of the input features for an LVQ classification system.
Feature selection has become the focus of much
research in areas of application for which datasets with
tens or hundreds of thousands of variables are available.
These areas include text processing of internet documents,
gene expression array analysis, and combinatorial


Source: Andonie, Razvan - Department of Computer Science, Central Washington University


Collections: Computer Technologies and Information Sciences