Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes

Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes

Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of resampling strategies and classifiers for imbalanced classification tasks. Existing AutoCIL methods focus solely on...

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Bibliographic Details
Main Authors: Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Class imbalance learning
multi-objective optimization
Bayesian optimization
automated machine learning
meta-learning
Gaussian processes
Online Access:https://ieeexplore.ieee.org/document/11087233/
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