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Class-imbalanced classifiers using ensembles of Gaussian processes and Gaussian process latent variable models

Citation Author(s):
Cassandra Heiselman, J. Gerald Quirk, Petar M. Djurić
Submitted by:
Liu Yang
Last updated:
25 June 2021 - 4:25pm
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Liu Yang
Paper Code:
MLSP-37.2

Abstract 

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.

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Dataset Files

EnGPC_GPLVM.pdf

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