A Survey of Predictive Modelling under Imbalanced Distributions

TL;DR

This survey reviews techniques for predictive modeling with highly imbalanced data distributions, highlighting challenges and solutions for classification and regression tasks in real-world applications.

Contribution

It provides a comprehensive taxonomy and comparison of existing methods for handling imbalanced data in predictive analytics, including both classification and regression.

Findings

Most techniques focus on classification tasks.

Methods include data resampling, cost-sensitive learning, and ensemble techniques.

The survey identifies gaps and future directions in imbalanced data modeling.

Abstract

Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least common values of this target variable are associated with events that are highly relevant for end users (e.g. fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which when associated with the rarity of some of them on the available training data creates serious problems to predictive modelling techniques. This paper presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey we discuss the main challenges raised by imbalanced distributions, describe the main approaches to these problems, propose a taxonomy of these methods and refer to some related problems within predictive modelling.