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arXiv · 1709.02327

Feature selection in high-dimensional dataset using MapReduce

Claudio Reggiani·Yann-Aël Le Borgne·Gianluca Bontempi
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Abstract

This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features.

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Claudio Reggiani, Yann-Aël Le Borgne, Gianluca Bontempi. 2017-09-07. Feature selection in high-dimensional dataset using MapReduce. https://arxiv.org/abs/1709.02327

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