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Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs
Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa
743 - 750
10.3233/978-1-61499-672-9-743
Frontiers in Artificial Intelligence and Applications
Volume 285: ECAI 2016
Abstract

In this paper we propose a method for scaling up filter-based 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.

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