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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Detalhes bibliográficos
Principais autores: Salmerón Cerdán, Antonio, Madsen, Anders L., Jensen, Frank, Langseth, Helge, Nielsen, Thomas D., Ramos López, Darío, Martínez, Ana M., Masegosa, Andrés R.
Formato: info:eu-repo/semantics/article
Idioma:English
Publicado em: 2017
Assuntos:
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
Acesso em linha:http://hdl.handle.net/10835/4916

Internet

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

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