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Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Non-linear Based Fuzzy Random Regression for Independent Variable Selection

Authors : Mohd Zaki Mohd Salikon, Nureize Arbaiy

Published in: Recent Advances on Soft Computing and Data Mining

Publisher: Springer International Publishing

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Abstract

This paper demonstrates a fuzzy random regression approach using genetic algorithm (FRR-GA) to select independent variable for regression model. The FRR-GA approach enables us to indicate the best coefficient values among regressor that indicate the best independent variable, which is important to build regression model. Additionally, the fuzzy random regression approach is employed to treat dual uncertainties due to the realization of such data in real application. This paper presents an algorithm reflecting the non-linear strategy in the fuzzy random regression model. A numerical example illustrates the proposed solution procedure whereby the result suggested several feasible solutions to the user.

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Metadata
Title
Non-linear Based Fuzzy Random Regression for Independent Variable Selection
Authors
Mohd Zaki Mohd Salikon
Nureize Arbaiy
Copyright Year
2017
Book
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_19

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