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Feature selection in high-dimensional dataset using MapReduce

par Reggiani, Claudio ;Le Borgne, Yann-Aël ;Bontempi, Gianluca
Référence Communications in computer and information science, 823, page (101-115)
Publication Publié, 2018

Article révisé par les pairs

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Résumé :

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.