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
- Indexing
- Scopus
- Impact Factor
- None
- Publication Type
- Both (Printed and Online)
- Volume
- --
- Year
- --
- Pages
- --