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On the joint-effect of class imbalance and overlap: a critical review | Artificial Intelligence Review

<!DOCTYPE html> <html lang="en" class="no-js"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="applicable-device" content="pc,mobile"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="robots" content="max-image-preview:large"> <meta name="access" content="No"> <meta name="360-site-verification" content="1268d79b5e96aecf3ff2a7dac04ad990" /> <title>On the joint-effect of class imbalance and overlap: a critical review | Artificial Intelligence Review</title> <meta name="twitter:site" content="@SpringerLink"/> <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:image:alt" content="Content cover image"/> <meta name="twitter:title" content="On the joint-effect of class imbalance and overlap: a critical review"/> <meta name="twitter:description" content="Artificial Intelligence Review - Current research on imbalanced data recognises that class imbalance is aggravated by other data intrinsic characteristics, among which class overlap stands out as..."/> <meta name="twitter:image" content="https://static-content.springer.com/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig1_HTML.png"/> <meta name="journal_id" content="10462"/> <meta name="dc.title" content="On the joint-effect of class imbalance and overlap: a critical review"/> <meta name="dc.source" content="Artificial Intelligence Review 2022 55:8"/> <meta name="dc.format" content="text/html"/> <meta name="dc.publisher" content="Springer"/> <meta name="dc.date" content="2022-03-24"/> <meta name="dc.type" content="OriginalPaper"/> <meta name="dc.language" content="En"/> <meta name="dc.copyright" content="2022 The Author(s), under exclusive licence to Springer Nature B.V."/> <meta name="dc.rights" content="2022 The Author(s), under exclusive licence to Springer Nature B.V."/> <meta name="dc.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="dc.description" content="Current research on imbalanced data recognises that class imbalance is aggravated by other data intrinsic characteristics, among which class overlap stands out as one of the most harmful. The combination of these two problems creates a new and difficult scenario for classification tasks and has been discussed in several research works over the past two decades. In this paper, we argue that despite some insightful information can be derived from related research, the joint-effect of class overlap and imbalance is still not fully understood, and advocate for the need to move towards a unified view of the class overlap problem in imbalanced domains. To that end, we start by performing a thorough analysis of existing literature on the joint-effect of class imbalance and overlap, elaborating on important details left undiscussed on the original papers, namely the impact of data domains with different characteristics and the behaviour of classifiers with distinct learning biases. This leads to the hypothesis that class overlap comprises multiple representations, which are important to accurately measure and analyse in order to provide a full characterisation of the problem. Accordingly, we devise two novel taxonomies, one for class overlap measures and the other for class overlap-based approaches, both resonating with the distinct representations of class overlap identified. This paper therefore presents a global and unique view on the joint-effect of class imbalance and overlap, from precursor work to recent developments in the field. It meticulously discusses some concepts taken as implicit in previous research, explores new perspectives in light of the limitations found, and presents new ideas that will hopefully inspire researchers to move towards a unified view on the problem and the development of suitable strategies for imbalanced and overlapped domains."/> <meta name="prism.issn" content="1573-7462"/> <meta name="prism.publicationName" content="Artificial Intelligence Review"/> <meta name="prism.publicationDate" content="2022-03-24"/> <meta name="prism.volume" content="55"/> <meta name="prism.number" content="8"/> <meta name="prism.section" content="OriginalPaper"/> <meta name="prism.startingPage" content="6207"/> <meta name="prism.endingPage" content="6275"/> <meta name="prism.copyright" content="2022 The Author(s), under exclusive licence to Springer Nature B.V."/> <meta name="prism.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="prism.url" content="https://link.springer.com/article/10.1007/s10462-022-10150-3"/> <meta name="prism.doi" content="doi:10.1007/s10462-022-10150-3"/> <meta name="citation_pdf_url" content="https://link.springer.com/content/pdf/10.1007/s10462-022-10150-3.pdf"/> <meta name="citation_fulltext_html_url" content="https://link.springer.com/article/10.1007/s10462-022-10150-3"/> <meta name="citation_journal_title" content="Artificial Intelligence Review"/> <meta name="citation_journal_abbrev" content="Artif Intell Rev"/> <meta name="citation_publisher" content="Springer Netherlands"/> <meta name="citation_issn" content="1573-7462"/> <meta name="citation_title" content="On the joint-effect of class imbalance and overlap: a critical review"/> <meta name="citation_volume" content="55"/> <meta name="citation_issue" content="8"/> <meta name="citation_publication_date" content="2022/12"/> <meta name="citation_online_date" content="2022/03/24"/> <meta name="citation_firstpage" content="6207"/> <meta name="citation_lastpage" content="6275"/> <meta name="citation_article_type" content="Article"/> <meta name="citation_language" content="en"/> <meta name="dc.identifier" content="doi:10.1007/s10462-022-10150-3"/> <meta name="DOI" content="10.1007/s10462-022-10150-3"/> <meta name="size" content="807510"/> <meta name="citation_doi" content="10.1007/s10462-022-10150-3"/> <meta name="citation_springer_api_url" content="http://api.springer.com/xmldata/jats?q=doi:10.1007/s10462-022-10150-3&amp;api_key="/> <meta name="description" content="Current research on imbalanced data recognises that class imbalance is aggravated by other data intrinsic characteristics, among which class overlap stands"/> <meta name="dc.creator" content="Santos, Miriam Seoane"/> <meta name="dc.creator" content="Abreu, Pedro Henriques"/> <meta name="dc.creator" content="Japkowicz, Nathalie"/> <meta name="dc.creator" content="Fern&#225;ndez, Alberto"/> <meta name="dc.creator" content="Soares, Carlos"/> <meta name="dc.creator" content="Wilk, Szymon"/> <meta name="dc.creator" content="Santos, Jo&#227;o"/> <meta name="dc.subject" content="Artificial Intelligence"/> <meta name="dc.subject" content="Computer Science, general"/> <meta name="citation_reference" content="citation_journal_title=IEEE Trans Knowl Data Eng; citation_title=To combat multi-class imbalanced problems by means of over-sampling techniques; citation_author=L Abdi, S Hashemi; citation_volume=28; citation_issue=1; citation_publication_date=2015; citation_pages=238-251; citation_doi=10.1109/TKDE.2015.2458858; citation_id=CR1"/> <meta name="citation_reference" content="Akbani R, Kwek S, Japkowicz N (2004) Applying support vector machines to imbalanced datasets. 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The combination of these two problems creates a new and difficult scenario for classification tasks and has been discussed in several research works over the past two decades. In this paper, we argue that despite some insightful information can be derived from related research, the joint-effect of class overlap and imbalance is still not fully understood, and advocate for the need to move towards a unified view of the class overlap problem in imbalanced domains. To that end, we start by performing a thorough analysis of existing literature on the joint-effect of class imbalance and overlap, elaborating on important details left undiscussed on the original papers, namely the impact of data domains with different characteristics and the behaviour of classifiers with distinct learning biases. This leads to the hypothesis that class overlap comprises multiple representations, which are important to accurately measure and analyse in order to provide a full characterisation of the problem. Accordingly, we devise two novel taxonomies, one for class overlap measures and the other for class overlap-based approaches, both resonating with the distinct representations of class overlap identified. This paper therefore presents a global and unique view on the joint-effect of class imbalance and overlap, from precursor work to recent developments in the field. It meticulously discusses some concepts taken as implicit in previous research, explores new perspectives in light of the limitations found, and presents new ideas that will hopefully inspire researchers to move towards a unified view on the problem and the development of suitable strategies for imbalanced and overlapped domains."/> <meta property="og:image" content="https://static-content.springer.com/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig1_HTML.png"/> <meta name="format-detection" content="telephone=no"> <link rel="apple-touch-icon" sizes="180x180" href=/oscar-static/img/favicons/darwin/apple-touch-icon-92e819bf8a.png> <link rel="icon" type="image/png" sizes="192x192" href=/oscar-static/img/favicons/darwin/android-chrome-192x192-6f081ca7e5.png> <link rel="icon" type="image/png" sizes="32x32" href=/oscar-static/img/favicons/darwin/favicon-32x32-1435da3e82.png> <link rel="icon" type="image/png" sizes="16x16" 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The combination of these two problems creates a new and difficult scenario for classification tasks and has been discussed in several research works over the past two decades. In this paper, we argue that despite some insightful information can be derived from related research, the joint-effect of class overlap and imbalance is still not fully understood, and advocate for the need to move towards a unified view of the class overlap problem in imbalanced domains. To that end, we start by performing a thorough analysis of existing literature on the joint-effect of class imbalance and overlap, elaborating on important details left undiscussed on the original papers, namely the impact of data domains with different characteristics and the behaviour of classifiers with distinct learning biases. This leads to the hypothesis that class overlap comprises multiple representations, which are important to accurately measure and analyse in order to provide a full characterisation of the problem. Accordingly, we devise two novel taxonomies, one for class overlap measures and the other for class overlap-based approaches, both resonating with the distinct representations of class overlap identified. This paper therefore presents a global and unique view on the joint-effect of class imbalance and overlap, from precursor work to recent developments in the field. 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class="u-visually-hidden">ORCID: </span>orcid.org/0000-0002-5912-963X</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Pedro_Henriques-Abreu-Aff1" data-author-popup="auth-Pedro_Henriques-Abreu-Aff1" data-author-search="Abreu, Pedro Henriques">Pedro Henriques Abreu</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Nathalie-Japkowicz-Aff2" data-author-popup="auth-Nathalie-Japkowicz-Aff2" data-author-search="Japkowicz, Nathalie">Nathalie Japkowicz</a><sup class="u-js-hide"><a href="#Aff2">2</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a 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class="u-js-hide"><a href="#Aff5">5</a></sup> &amp; </li><li class="c-article-author-list__show-more" aria-label="Show all 7 authors for this article" title="Show all 7 authors for this article">…</li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Jo_o-Santos-Aff6-Aff7" data-author-popup="auth-Jo_o-Santos-Aff6-Aff7" data-author-search="Santos, João">João Santos</a><sup class="u-js-hide"><a href="#Aff6">6</a>,<a href="#Aff7">7</a></sup> </li></ul><button aria-expanded="false" class="c-article-author-list__button"><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-down-medium"></use></svg><span>Show authors</span></button> <div data-test="article-metrics"> <ul class="app-article-metrics-bar u-list-reset"> <li class="app-article-metrics-bar__item"> <p 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app-article-metrics-bar__item--metrics"> <p class="app-article-metrics-bar__details"><a href="/article/10.1007/s10462-022-10150-3/metrics" data-track="click" data-track-action="view metrics" data-track-label="link" rel="nofollow">Explore all metrics <svg class="u-icon app-article-metrics-bar__arrow-icon" width="24" height="24" aria-hidden="true" focusable="false"> <use xlink:href="#icon-eds-i-arrow-right-medium"></use> </svg></a></p> </li> </ul> </div> <div class="u-mt-32"> </div> </header> </div> <div data-article-body="true" data-track-component="article body" class="c-article-body"> <section aria-labelledby="Abs1" data-title="Abstract" lang="en"><div class="c-article-section" id="Abs1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Abs1">Abstract</h2><div class="c-article-section__content" id="Abs1-content"><p>Current research on imbalanced data recognises that class imbalance is aggravated by other data intrinsic characteristics, among which class overlap stands out as one of the most harmful. The combination of these two problems creates a new and difficult scenario for classification tasks and has been discussed in several research works over the past two decades. In this paper, we argue that despite some insightful information can be derived from related research, the joint-effect of class overlap and imbalance is still not fully understood, and advocate for the need to move towards a unified view of the class overlap problem in imbalanced domains. To that end, we start by performing a thorough analysis of existing literature on the joint-effect of class imbalance and overlap, elaborating on important details left undiscussed on the original papers, namely the impact of data domains with different characteristics and the behaviour of classifiers with distinct learning biases. This leads to the hypothesis that class overlap comprises multiple representations, which are important to accurately measure and analyse in order to provide a full characterisation of the problem. Accordingly, we devise two novel taxonomies, one for class overlap measures and the other for class overlap-based approaches, both resonating with the distinct representations of class overlap identified. This paper therefore presents a global and unique view on the joint-effect of class imbalance and overlap, from precursor work to recent developments in the field. It meticulously discusses some concepts taken as implicit in previous research, explores new perspectives in light of the limitations found, and presents new ideas that will hopefully inspire researchers to move towards a unified view on the problem and the development of suitable strategies for imbalanced and overlapped domains.</p></div></div></section> <div class="c-notes"> <p class="c-notes__text c-status-message--info"> <svg width="24" height="24" focusable="false" role="img" aria-hidden="true" class="c-status-message__icon"> <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-info-filled-medium"></use> </svg> This is a preview of subscription content, <a id="test-login-banner-link" href="//wayf.springernature.com?redirect_uri&#x3D;https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs10462-022-10150-3%3Ferror%3Dcookies_not_supported%26code%3Df0f2f7a0-2ee6-4664-810f-566decc5e373" data-track="click" data-track-action="login" data-track-label="link" 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type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig15_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig15_HTML.png" alt="" loading="lazy" width="312" height="143"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-16"><figure><figcaption><b id="Fig16" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 16</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig16_HTML.png?as=webp"><img 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height="93"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-18"><figure><figcaption><b id="Fig18" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 18</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig18_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig18_HTML.png" alt="" loading="lazy" width="312" height="135"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-19"><figure><figcaption><b id="Fig19" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 19</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig19_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig19_HTML.png" alt="" loading="lazy" width="312" height="152"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-20"><figure><figcaption><b id="Fig20" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 20</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig20_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig20_HTML.png" alt="" loading="lazy" width="312" height="227"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-21"><figure><figcaption><b id="Fig21" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 21</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig21_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig21_HTML.png" alt="" loading="lazy" width="312" height="141"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-22"><figure><figcaption><b id="Fig22" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 22</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig22_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig22_HTML.png" alt="" loading="lazy" width="312" height="230"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-23"><figure><figcaption><b id="Fig23" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 23</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig23_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig23_HTML.png" alt="" loading="lazy" width="312" height="159"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-24"><figure><figcaption><b id="Fig24" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 24</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig24_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig24_HTML.png" alt="" loading="lazy" width="312" height="157"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-25"><figure><figcaption><b id="Fig25" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 25</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig25_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig25_HTML.png" alt="" loading="lazy" width="312" height="96"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-26"><figure><figcaption><b id="Fig26" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 26</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10462-022-10150-3/MediaObjects/10462_2022_10150_Fig26_HTML.png?as=webp"><img 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page" data-track-label="Artificial Intelligence">Artificial Intelligence</a> </li> </ul> </section> <section data-title="Notes"><div class="c-article-section" id="notes-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="notes">Notes</h2><div class="c-article-section__content" id="notes-content"><ol class="c-article-footnote c-article-footnote--listed"><li class="c-article-footnote--listed__item" id="Fn1" data-counter="1."><div class="c-article-footnote--listed__content"><p>The reader may find supporting information in the supplementary material online at <a href="https://student.dei.uc.pt/%7emiriams/pdf-files/AIR_2021_Appendix.pdf">https://student.dei.uc.pt/~miriams/pdf-files/AIR_2021_Appendix.pdf</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn2" data-counter="2."><div class="c-article-footnote--listed__content"><p>The interested reader may find detailed information on the performance of each classifier in the supplementary material provided online at <a href="https://student.dei.uc.pt/%7emiriams/pdf-files/AIR_2021_Appendix.pdf">https://student.dei.uc.pt/~miriams/pdf-files/AIR_2021_Appendix.pdf</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn3" data-counter="3."><div class="c-article-footnote--listed__content"><p><a href="https://github.com/miriamspsantos/pycol">https://github.com/miriamspsantos/pycol</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn4" data-counter="4."><div class="c-article-footnote--listed__content"><p><a href="https://archive.ics.uci.edu">https://archive.ics.uci.edu</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn5" data-counter="5."><div class="c-article-footnote--listed__content"><p><a href="https://www.kaggle.com">https://www.kaggle.com</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn6" data-counter="6."><div class="c-article-footnote--listed__content"><p><a href="http://keel.es">http://keel.es</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn7" data-counter="7."><div class="c-article-footnote--listed__content"><p><a href="https://www.openml.org">https://www.openml.org</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn8" data-counter="8."><div class="c-article-footnote--listed__content"><p><a href="https://github.com/miriamspsantos/datagenerator">https://github.com/miriamspsantos/datagenerator</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn9" data-counter="9."><div class="c-article-footnote--listed__content"><p><a href="https://github.com/miriamspsantos/open-source-imbalance-overlap">https://github.com/miriamspsantos/open-source-imbalance-overlap</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn10" data-counter="10."><div class="c-article-footnote--listed__content"><p><a href="https://github.com/miriamspsantos/pycol">https://github.com/miriamspsantos/pycol</a>.</p></div></li></ol></div></div></section><div id="MagazineFulltextArticleBodySuffix"><section aria-labelledby="Bib1" data-title="References"><div class="c-article-section" id="Bib1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Bib1">References</h2><div class="c-article-section__content" id="Bib1-content"><div data-container-section="references"><ul class="c-article-references" data-track-component="outbound reference" data-track-context="references section"><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR1">Abdi L, Hashemi S (2015) To combat multi-class imbalanced problems by means of over-sampling techniques. 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This work is also partially supported by Andalusian frontier regional project A-TIC-434-UGR20 and by the Spanish Ministry of Science and Technology under project PID2020-119478GB-I00 including European Regional Development Funds. This work was also partially funded by the project Safe Cities-Inovação para Construir Cidades Seguras, with the reference POCI-01-0247-FEDER-041435, co-funded by the European Regional Development Fund (ERDF), through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement. The work is further supported by the FCT Research Grant SFRH/BD/138749/2018.</p></div></div></section><section aria-labelledby="author-information" data-title="Author information"><div class="c-article-section" id="author-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="author-information">Author information</h2><div class="c-article-section__content" id="author-information-content"><h3 class="c-article__sub-heading" id="affiliations">Authors and Affiliations</h3><ol class="c-article-author-affiliation__list"><li id="Aff1"><p class="c-article-author-affiliation__address">Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal</p><p class="c-article-author-affiliation__authors-list">Miriam Seoane Santos &amp; Pedro Henriques Abreu</p></li><li id="Aff2"><p class="c-article-author-affiliation__address">Department of Computer Science, American University, Washington, DC, 20016, USA</p><p class="c-article-author-affiliation__authors-list">Nathalie Japkowicz</p></li><li id="Aff3"><p class="c-article-author-affiliation__address">Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain</p><p class="c-article-author-affiliation__authors-list">Alberto Fernández</p></li><li id="Aff4"><p class="c-article-author-affiliation__address">Fraunhofer Portugal AICOS and LIACC, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal</p><p class="c-article-author-affiliation__authors-list">Carlos Soares</p></li><li id="Aff5"><p class="c-article-author-affiliation__address">Institute of Computing Science, Poznan University of Technology, Poznan, Poland</p><p class="c-article-author-affiliation__authors-list">Szymon Wilk</p></li><li id="Aff6"><p class="c-article-author-affiliation__address">IPO-Porto Research Centre (CI-IPOP), Porto, Portugal</p><p class="c-article-author-affiliation__authors-list">João Santos</p></li><li id="Aff7"><p class="c-article-author-affiliation__address">Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto, Porto, Portugal</p><p class="c-article-author-affiliation__authors-list">João Santos</p></li></ol><div class="u-js-hide u-hide-print" data-test="author-info"><span class="c-article__sub-heading">Authors</span><ol class="c-article-authors-search u-list-reset"><li id="auth-Miriam_Seoane-Santos-Aff1"><span class="c-article-authors-search__title u-h3 js-search-name">Miriam Seoane Santos</span><div class="c-article-authors-search__list"><div class="c-article-authors-search__item c-article-authors-search__list-item--left"><a href="/search?dc.creator=Miriam%20Seoane%20Santos" class="c-article-button" data-track="click" data-track-action="author link - 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id="citeas">Cite this article</h3><p class="c-bibliographic-information__citation">Santos, M.S., Abreu, P.H., Japkowicz, N. <i>et al.</i> On the joint-effect of class imbalance and overlap: a critical review. <i>Artif Intell Rev</i> <b>55</b>, 6207–6275 (2022). https://doi.org/10.1007/s10462-022-10150-3</p><p class="c-bibliographic-information__download-citation u-hide-print"><a data-test="citation-link" data-track="click" data-track-action="download article citation" data-track-label="link" data-track-external="" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1007/s10462-022-10150-3?format=refman&amp;flavour=citation">Download citation<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p><ul class="c-bibliographic-information__list" data-test="publication-history"><li class="c-bibliographic-information__list-item"><p>Published<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2022-03-24">24 March 2022</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Issue Date<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2022-12">December 2022</time></span></p></li><li class="c-bibliographic-information__list-item c-bibliographic-information__list-item--full-width"><p><abbr title="Digital Object Identifier">DOI</abbr><span class="u-hide">: </span><span class="c-bibliographic-information__value">https://doi.org/10.1007/s10462-022-10150-3</span></p></li></ul><div data-component="share-box"><div class="c-article-share-box u-display-none" hidden=""><h3 class="c-article__sub-heading">Share this article</h3><p class="c-article-share-box__description">Anyone you share the following link with will be able to read this content:</p><button 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c-article-share-box__additional-info"> Provided by the Springer Nature SharedIt content-sharing initiative </p></div></div><h3 class="c-article__sub-heading">Keywords</h3><ul class="c-article-subject-list"><li class="c-article-subject-list__subject"><span><a href="/search?query=Class%20imbalance&amp;facet-discipline=&#34;Computer%20Science&#34;" data-track="click" data-track-action="view keyword" data-track-label="link">Class imbalance</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Class%20overlap&amp;facet-discipline=&#34;Computer%20Science&#34;" data-track="click" data-track-action="view keyword" data-track-label="link">Class overlap</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Data%20intrinsic%20characteristics&amp;facet-discipline=&#34;Computer%20Science&#34;" data-track="click" data-track-action="view keyword" data-track-label="link">Data intrinsic characteristics</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Class%20overlap%20complexity%20measures&amp;facet-discipline=&#34;Computer%20Science&#34;" data-track="click" data-track-action="view keyword" data-track-label="link">Class overlap complexity measures</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Class%20overlap-based%20approaches&amp;facet-discipline=&#34;Computer%20Science&#34;" data-track="click" data-track-action="view keyword" data-track-label="link">Class overlap-based approaches</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Class%20overlap%20representations&amp;facet-discipline=&#34;Computer%20Science&#34;" data-track="click" data-track-action="view keyword" data-track-label="link">Class overlap representations</a></span></li></ul><div data-component="article-info-list"></div></div></div></div></div></section> </div> </main> <div class="c-article-sidebar u-text-sm u-hide-print 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