arXiv Open Access 2017

Feature selection in high-dimensional dataset using MapReduce

Claudio Reggiani Yann-Aël Le Borgne Gianluca Bontempi

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.DC cs.LG stat.ML

Penulis (3)

Claudio Reggiani

Yann-Aël Le Borgne

Gianluca Bontempi

Format Sitasi

APA MLA BibTeX

Reggiani, C., Borgne, Y.L., Bontempi, G. (2017). Feature selection in high-dimensional dataset using MapReduce. https://arxiv.org/abs/1709.02327

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓