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2018 | OriginalPaper | Chapter

Parallel C–Fuzzy Random Forest

Authors : Łukasz Gadomer, Zenon A. Sosnowski

Published in: Computer Information Systems and Industrial Management

Publisher: Springer International Publishing

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Abstract

The C–fuzzy random forest is a novel ensemble classifier which uses C-fuzzy decision trees as unit classifiers. The main problem connected with this classifier is a relatively long learning process time. In this paper the method of reducing the C–fuzzy random forest’s learning time is proposed. Authors proposed and described the method of parallelization of this classifier’s learning process by generating trees which are the parts of the forest in separate threads. The experiments which were designed to check the effectiveness of the proposed method were performed and the results were presented and discussed.

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Footnotes
1
There is also another decision–making strategy which assumes making the single decision by the whole forest. It is described in [2].
 
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Metadata
Title
Parallel C–Fuzzy Random Forest
Authors
Łukasz Gadomer
Zenon A. Sosnowski
Copyright Year
2018
Book
Computer Information Systems and Industrial Management

Print ISBN: 978-3-319-99953-1

Electronic ISBN: 978-3-319-99954-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99954-8_22

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