Oversampling of imbalanced univariate time series classification data using integrated ESPO and ADASYN methods. Enhanced Structure Preserving Oversampling (ESPO) is used to generate a large percentage of the synthetic minority samples from univariate labeled time series under the modeling assumption that the predictors are Gaussian. ESPO estimates the covariance structure of the minority-class samples and applies a spectral filer to reduce noise. Adaptive Synthetic (ADASYN) sampling approach is a nearest neighbor interpolation approach which is subsequently applied to the ESPO samples. This code is ported from a 'MATLAB' implementation by Cao et al. <doi:10.1109/TKDE.2013.37> and adapted for use with Recurrent Neural Networks implemented in 'TensorFlow'.
Package details |
|
---|---|
Author | Matthew Dixon [ctb], Diego Klabjan [ctb], Lan Wei [aut, trl, cre] |
Maintainer | Lan Wei <lweicdsor@gmail.com> |
License | GPL-3 |
Version | 0.0.1 |
URL | https://github.com/lweicdsor/OSTSC |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.