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Found 4 papers, 2 papers with code
Local overlap reduction procedure for dynamic ensemble selection
Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class.
Multi-label learning for dynamic model type recommendation
Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant model types based on the local data complexity of the region surrounding each test sample.
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
Online local pool generation for dynamic classifier selection: an extended version
Thus, we propose in this work an online pool generation method that produces a locally accurate pool for test samples in difficult regions of the feature space.
