Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs
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Salmerón Cerdán, Antonio; Madsen, Anders L.; Jensen, Frank; Langseth, Helge; Nielsen, Thomas D.; [et al.]Fecha
2016Resumen
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 calculations between the available computing units is determined based on balanced incomplete
block designs, a strategy first developed within the area of statistical design of experiments. We show the scalability of our method through a series of experiments on synthetic and real-world datasets.
Palabra/s clave
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