Dynamic Classifier Selection for Data with Skewed Class Distribution Using Imbalance Ratio and Euclidean Distance

Imbalanced data analysis remains one of the critical challenges in machine learning. This work aims to adapt the concept of Dynamic Classifier Selection (dcs) to the pattern classification task with the skewed class distribution. Two methods, using the similarity (distance) to the reference instances and class imbalance ratio to select the most confident classifier for a given observation, have been proposed. Both approaches come in two modes, one based on the k-Nearest Oracles (knora) and the other also considering those cases where the classifier makes a mistake. The proposed methods were evaluated based on computer experiments carried out on 41 datasets with a high imbalance ratio. The obtained results and statistical analysis confirm the usefulness of the proposed solutions.