survey

A Survey of Predictive Modeling on Imbalanced Domains

Authors:

Luís Torgo, and

Rita P. RibeiroAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 49, Issue 2

Article No.: 31, Pages 1 - 50

https://doi.org/10.1145/2907070

Published: 13 August 2016 Publication History

Abstract

Many real-world data-mining applications involve obtaining predictive models using datasets 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 modeling techniques. This article 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 domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.

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Index Terms

A Survey of Predictive Modeling on Imbalanced Domains
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Cost-sensitive learning

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 49, Issue 2

June 2017

747 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2966278

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Publication History

Published: 13 August 2016

Accepted: 01 March 2016

Revised: 01 March 2016

Received: 01 May 2015

Published in CSUR Volume 49, Issue 2

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