Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models

Liu Yang; Cassandra Heiselman; J. Gerald Quirk; Petar M. Djuric

ICASSP 2021accepted0 citations

Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models

Liu Yang, Cassandra Heiselman, J. Gerald Quirk, Petar M. Djuric

Abstract

Classification with imbalanced data is a common and challenging problem in many practical machine learning problems. Ensemble learning is a popular solution where the results from multiple base classifiers are synthesized to reduce the effect of a possibly skewed distribution of the training set. In this paper, binary classifiers based on Gaussian processes are chosen as bases for inferring the predictive distributions of test latent variables. We apply a Gaussian process latent variable model where the outputs of the Gaussian processes are used for making the final decision. The tests of the new method in both synthetic and real data sets show improved performance over standard approaches.

BibTeX

@inproceedings{icassp2021_classimbalancedc,
  title = {Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models},
  author = {Liu Yang and Cassandra Heiselman and J. Gerald Quirk and Petar M. Djuric},
  booktitle = {ICASSP 2021},
  year = {2021}
}