Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning

TL;DR

This paper introduces two stochastic multi-stage classifier architectures designed for imbalanced multiclass datasets, especially in text classification, improving minority class detection and proposing a new evaluation metric.

Contribution

It presents novel multi-stage classifier models (CMC and CMC-M) tailored for imbalanced multiclass problems, with enhanced minority class recognition and a new performance metric SG-Mean.

Findings

Improved classification results on six datasets.

Enhanced minority class detection in skewed datasets.

Proposed SG-Mean metric to address G-Mean limitations.

Abstract

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.