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CLASS-IMBALANCED CLASSIFIERS USING ENSEMBLES OF GAUSSIAN PROCESSES AND GAUSSIAN PROCESS LATENT VARIABLE MODELS.
Yang, Liu; Heiselman, Cassandra; Quirk, J Gerald; Djuric, Petar M.
Affiliation
  • Yang L; Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
  • Heiselman C; Department of Obstetrics, Gynecology and Reproductive Medicine, Stony Brook, NY 11794, USA.
  • Quirk JG; Department of Obstetrics, Gynecology and Reproductive Medicine, Stony Brook, NY 11794, USA.
  • Djuric PM; Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
Article in En | MEDLINE | ID: mdl-34712104
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Proc IEEE Int Conf Acoust Speech Signal Process Year: 2021 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Proc IEEE Int Conf Acoust Speech Signal Process Year: 2021 Document type: Article Affiliation country: United States Country of publication: United States