Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs

In this paper we propose a method for scaling up filterbased feature selection in classification problems. We use the conditional mutual information as filter measure and show how the required statistics can be computed in parallel avoiding unnecessary calculations. The distribution of the calculat...

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Détails bibliographiques
Auteurs principaux: Salmerón Cerdán, Antonio, Madsen, Anders L., Jensen, Frank, Langseth, Helge, Nielsen, Thomas D., Ramos López, Darío, Martínez, Ana M., Masegosa, Andrés R.
Format: info:eu-repo/semantics/article
Langue:English
Publié: 2017
Sujets:
Bandpass filters
Design of experiments
Balanced incomplete block design
Computing units
Conditional mutual information
Filter-based
Real-world datasets
Scaling-up
Statistical design of experiments
Accès en ligne:http://hdl.handle.net/10835/4916

Internet

http://hdl.handle.net/10835/4916

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