Inferring linear and nonlinear Interaction networks using neighborhood support vector machines
Publication Type
Original research
Authors

In this paper, we consider modelling interaction between a
set of variables in the context of time series and high dimension. We
suggest two approaches. The first is similar to the neighborhood lasso
when the lasso model is replaced by a support vector machine (SVMs).
The second is a restricted Bayesian network adapted for time series.
We show the efficiency of our approaches by simulations using linear,
nonlinear data set and a mixture of both.

Journal
Title
2021 International Conference on Engineering and Emerging Technologies (ICEET)
Publisher
IEEE
Publisher Country
United States of America
Publication Type
Both (Printed and Online)
Volume
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Year
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Pages
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