Ordinal Classification of Imbalanced Data with Application in Emergency and Disaster Information Services
Sungil Kim, Heeyoung Kim, Younghwan Namkoong · IEEE Intelligent Systems · 2016
Previous ordinal classification methods implicitly assume that the class distribution within a dataset is balanced, which is often not the case for real-world datasets. If the dataset is imbalanced, the previous methods tend to be biased toward the majority class. The authors propose a new method for ordinal classification that attempts to mitigate the impact of imbalanced datasets. They propose a modified version of the weighted k-nearest neighbors method that determines the class membership using the αth quantile of the estimated class probability distribution and thus mitigates the impact of the class imbalance.