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When the sizes of those subgroups become small, for example in rare diseases, imbalances between the classes are more the rule than the exception and make statistical classification problematic when the error rate of the minority class is high. Many observations are classified as belonging to the majority class, while the error rate of the majority class is low. This case study aims to investigate class imbalance for Random Forests and Powered Partial Least Squares Discriminant Analysis (PPLS\u2010DA) and to evaluate the performance of these classifiers when they are combined with methods to compensate imbalance (sampling methods, cost\u2010sensitive learning approaches). We evaluate all approaches with a scoring system taking the classification results into consideration. This case study is based on one high\u2010dimensional multiplex autoimmune assay dataset describing immune response to antigens and consisting of two classes of patients: Rheumatoid Arthritis (RA) and Systemic Lupus Erythemathodes (SLE). Datasets with varying degrees of imbalance are created by successively reducing the class of RA patients. Our results indicate possible benefit of cost\u2010sensitive learning approaches for Random Forests. Although further research is needed to verify our findings by investigating other datasets or large\u2010scale simulation studies, we claim that this work has the potential to increase awareness of practitioners to this problem of class imbalance and stresses the importance of considering methods to compensate class imbalance.<\/jats:p>","DOI":"10.1002\/bimj.201600207","type":"journal-article","created":{"date-parts":[[2017,6,19]],"date-time":"2017-06-19T07:12:44Z","timestamp":1497856364000},"page":"948-966","source":"Crossref","is-referenced-by-count":3,"title":["Multivariate binary classification of imbalanced datasets\u2014A case study based on high\u2010dimensional multiplex autoimmune assay data"],"prefix":"10.1002","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1723-9715","authenticated-orcid":false,"given":"Laura","family":"Schlieker","sequence":"first","affiliation":[{"name":"ClinStat GmbH Max\u2010Planck\u2010Str. 22a, 50858 Cologne, formerly Protagen AG, Otto\u2010Hahn\u2010Str. 15 44227 Dortmund Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Telaar","sequence":"additional","affiliation":[{"name":"Berufskolleg am Wassertum 46399 Bocholt, formerly Protagen AG, Otto\u2010Hahn\u2010Str. 15 44227 Dortmund Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelika","family":"Lueking","sequence":"additional","affiliation":[{"name":"Protagen AG Otto\u2010Hahn\u2010Str. 15 44227 Dortmund Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Schulz\u2010Knappe","sequence":"additional","affiliation":[{"name":"Protagen AG Otto\u2010Hahn\u2010Str. 15 44227 Dortmund Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmen","family":"Theek","sequence":"additional","affiliation":[{"name":"Chiltern International GmbH Am Kronberger Hang 3, 65824 Schwalbach, formerly Protagen AG, Otto\u2010Hahn\u2010Str. 15 44227 Dortmund Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katja","family":"Ickstadt","sequence":"additional","affiliation":[{"name":"Department of Mathematical Statistics with Applications in Biometrics, Faculty of Statistics Technical University Dortmund Vogelpothsweg 87 44227 Dortmund Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2017,6,19]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cem.785"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"},{"key":"e_1_2_10_4_1","article-title":"SMOTE for high\u2010dimensional class\u2010imbalanced data","volume":"14","author":"Blagus R.","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"e_1_2_10_5_1","volume-title":"Classification and Regression Trees","author":"Breiman L.","year":"1984"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_2_10_8_1","unstructured":"Chen C. 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