Strengthening the Forward Variable Selection Stopping Criterion

Herrera, Luis Javier; Rubio, G; Pomares, H; Paechter, B; Guill�n, A; Rojas, I

Abstract

Given any modeling problem, variable selection is a preprocess step that selects the most relevant variables with respect to the output variable. Forward selection is the most straightforward strategy for variable selection; its application using the mutual information is simple, intuitive and effective, and is commonly used in the machine learning literature. However the problem of when to stop the forward process doesn’t have a direct satisfactory solution due to the inaccuracies of the Mutual Information estimation, specially as the number of variables considered increases. This work proposes a modified stopping criterion for this variable selection methodology that uses the Markov blanket concept. As it will be shown, this approach can increase the performance and applicability of the stopping criterion of a forward selection process using mutual information.

Citation

Herrera, L. J., Rubio, G., Pomares, H., Paechter, B., Guillén, A., & Rojas, I. Strengthening the Forward Variable Selection Stopping Criterion

• Presentation Conference Type: Conference Paper (published)
• Publication Date: 2009
• Deposit Date: Aug 1, 2016
• Electronic ISSN: 0302-9743
• Publisher: Springer
• Pages: 215-224
• Series Title: Lecture Notes in Computer Science
• Series Number: 5769
• Series ISSN: 0302-9743
• Book Title: Artificial Neural Networks – ICANN 2009
• ISBN: 978-3-642-04276-8
• DOI: https://doi.org/10.1007/978-3-642-04277-5_22
• Keywords: Variable selection, mutual Information, function approximation,
• Public URL: http://researchrepository.napier.ac.uk/Output/321944