Journal
ACM COMPUTING SURVEYS
Volume 49, Issue 2, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2907070
Keywords
Imbalanced domains; rare cases; classification; regression; performance metrics
Categories
Funding
- European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalisation-COMPETE 2020 Programme [POCI-01-0145-FEDER-006961]
- North Portugal Regional Operational Programme (ON.2 O Novo Norte) under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF)
- national funds, through the Portuguese funding agency, Fundacao para a Ciencia e a Tecnologia (FCT) [NORTE-07-0124-FEDER-000059]
- Fundacao para a Ciencia e Tecnologia (FCT), Portugal [PD/BD/105788/2014]
- sabbatical scholarship from the Fundacao para a Ciencia e Tecnologia (FCT) [SFRH/BSAB/113896/2015]
- Fundação para a Ciência e a Tecnologia [SFRH/BSAB/113896/2015, PD/BD/105788/2014] Funding Source: FCT
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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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