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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

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CLASS-IMBALANCED CLASSIFIERS USING ENSEMBLES OF GAUSSIAN PROCESSES AND GAUSSIAN PROCESS LATENT VARIABLE MODELS.

Liu Yang¹, Cassandra Heiselman², J Gerald Quirk²

¹Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)

|October 29, 2021

Summary

This summary is machine-generated.

This study introduces a novel Gaussian process latent variable model for imbalanced classification. The ensemble method improves predictive performance on skewed datasets compared to standard approaches.

Keywords:

Gaussian process latent variable model Gaussian processes classifier class-imbalanced learning ensemble learning

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Area of Science:

Machine Learning
Statistical Modeling

Background:

Imbalanced data classification presents significant challenges in practical machine learning.
Ensemble learning methods offer a robust solution by combining multiple base classifiers to mitigate skewed training data distributions.

Purpose of the Study:

To develop and evaluate a novel ensemble learning method for imbalanced classification problems.
To leverage Gaussian processes within a latent variable model framework for improved predictive accuracy.

Main Methods:

Utilized binary classifiers based on Gaussian processes as base learners.
Applied a Gaussian process latent variable model to infer predictive distributions of test variables.
Synthesized results from multiple Gaussian process classifiers for the final decision.

Main Results:

The proposed Gaussian process latent variable model demonstrated improved performance.
The method showed enhanced results on both synthetic and real-world imbalanced datasets.
Outperformed standard classification approaches in handling skewed data distributions.

Conclusions:

The novel Gaussian process-based ensemble method is effective for imbalanced classification.
This approach offers a promising alternative for machine learning tasks with skewed data.
Further research can explore extensions of this model for more complex classification scenarios.