Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning
This work addresses a domain-specific problem in text classification, such as event detection, for datasets with many classes and multiple skewed classes, but it appears incremental as it builds on existing sampling techniques and metrics.
The authors tackled imbalanced multiclass learning with multiple skewed majority classes by proposing two stochastic architectural models (CMC and CMC-M) combined with pre-processing sampling techniques, which improved classification results on six datasets and introduced a new metric SG-Mean to address limitations of G-Mean.
In this work, we propose two stochastic architectural models (CMC and CMC-M) with two layers of classifiers applicable to datasets with one and multiple skewed classes. This distinction becomes important when the datasets have a large number of classes. Therefore, we present a novel solution to imbalanced multiclass learning with several skewed majority classes, which improves minority classes identification. This fact is particularly important for text classification tasks, such as event detection. Our models combined with pre-processing sampling techniques improved the classification results on six well-known datasets. Finally, we have also introduced a new metric SG-Mean to overcome the multiplication by zero limitation of G-Mean.