The SAMME.C2 algorithm for severely imbalanced multi-class classification

TL;DR

This paper introduces SAMME.C2, a novel multi-class classification algorithm designed specifically for severely imbalanced datasets, combining boosting techniques to improve minority class prediction accuracy.

Contribution

The paper presents the SAMME.C2 algorithm, a new boosting-based method tailored for severely imbalanced multi-class classification problems, with formal scientific and statistical foundations.

Findings

Consistently superior performance across various imbalance scenarios

Effective handling of minority class prediction challenges

Demonstrated robustness in numerical experiments

Abstract

Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. There is an increasing growth of real-world classification problems with severely imbalanced class distributions. In this case, minority classes have much fewer observations to learn from than those from majority classes. Despite this sparsity, a minority class is often considered the more interesting class yet developing a scientific learning algorithm suitable for the observations presents countless challenges. In this article, we suggest a novel multi-class classification algorithm specialized to handle severely imbalanced classes based on the method we refer to as SAMME.C2. It blends the flexible mechanics of the boosting techniques from SAMME algorithm, a multi-class classifier, and Ada.C2 algorithm, a cost-sensitive binary classifier designed to address highly class imbalances. Not only do we provide the resulting algorithm but we also establish scientific and statistical formulation of our proposed SAMME.C2 algorithm. Through numerical experiments examining various degrees of classifier difficulty, we demonstrate consistent superior performance of our proposed model.