On estimating mutual information for feature selection

Authors:
Erik Schaffernicht;Robert Kaltenhaeuser;Saurabh Shekhar Verma;Horst-Michael Gross
Affiliations:
Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Germany;Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Germany;College of Technology, GBPUAT, Pantnagar, India;College of Technology, GBPUAT, Pantnagar, India
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

Citing 1
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Low bias histogram-based estimation of mutual information for feature selection

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Abstract

Mutual Information (MI) is a powerful concept from information theory used in many application fields. For practical tasks it is often necessary to estimate the Mutual Information from available data. We compare state of the art methods for estimating MI from continuous data, focusing on the usefulness for the feature selection task. Our results suggest that many methods are practically relevant for feature selection tasks regardless of their theoretic limitations or benefits.