Ultra-imbalanced classification guided by statistical information

TL;DR

This paper introduces ultra-imbalanced classification (UIC), a new framework for imbalanced learning that considers population-level data and proposes a robust loss function, validated through theoretical analysis and extensive experiments.

Contribution

It proposes the UIC framework and a novel Tunable Boosting Loss that effectively handles ultra-imbalanced data, especially in industrial applications.

Findings

Tunable Boosting Loss resists data imbalance under UIC.

The framework is validated on public and industrial datasets.

Theoretical analysis links loss functions to statistical information.

Abstract

Imbalanced data are frequently encountered in real-world classification tasks. Previous works on imbalanced learning mostly focused on learning with a minority class of few samples. However, the notion of imbalance also applies to cases where the minority class contains abundant samples, which is usually the case for industrial applications like fraud detection in the area of financial risk management. In this paper, we take a population-level approach to imbalanced learning by proposing a new formulation called \emph{ultra-imbalanced classification} (UIC). Under UIC, loss functions behave differently even if infinite amount of training samples are available. To understand the intrinsic difficulty of UIC problems, we borrow ideas from information theory and establish a framework to compare different loss functions through the lens of statistical information. A novel learning objective termed Tunable Boosting Loss is developed which is provably resistant against data imbalance under UIC, as well as being empirically efficient verified by extensive experimental studies on both public and industrial datasets.