Random forest implementation and optimization for Big Data analytics on LexisNexis’s high performance computing cluster platform-Reference-Cited by-同舟云学术

Random forest implementation and optimization for Big Data analytics on LexisNexis’s high performance computing cluster platform

Published:2019-07-30 Issue:1 Volume:6 Page:
ISSN:2196-1115
Container-title:Journal of Big Data
language:en
Short-container-title:J Big Data

Author:

Herrera Victor M.^ORCID,Khoshgoftaar Taghi M.,Villanustre Flavio,Furht Borko

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Link

http://link.springer.com/content/pdf/10.1186/s40537-019-0232-1.pdf

Reference37 articles.

1. Middleton AM, Bayliss DA, Halliday G, Chala A, Furht B. The HPCC/ECL platform for Big Data. In: Big Data technologies and applications. New York: Springer; 2016. p. 159–83.

2. Lexis Nexis Risk Solutions. HPCC Systems: Introduction to HPCC. 2011. http://cdn.hpccsystems.com/whitepapers/wp_introduction_HPCC.pdf .

3. HPCC Systems. ECL Language Reference. 2011. https://hpccsystems.com/training/documentation/ecl-language-reference/html .

4. HPCC Systems. ECL Machine Learning Library. 2010. https://github.com/hpcc-systems/ecl-ml .

5. CAKE: Center for Advanced Knowledge Enablement. CAKE—developing machine learning algorithms on HPCC/ECL Platform. FAU, 2012. http://mlab.fau.edu/cake/developing-machine-learning-algorithms-on-hpcc-ecl-platform/ .

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