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We have been able to significantly reduce the set of initial features, using the proposed ensemble feature selection method. The best results are obtained using <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\chi ^2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>\u03c7<\/mml:mi>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, Random Forest, and Runoff as the based selector, classifier, and aggregation method, respectively. The classification performance of the best model trained on the selected features set results in 0.939 recall, 0.866 specificity, 0.903 accuracy, 0.875 precision, and 0.906 F1-score.<\/jats:p>","DOI":"10.1007\/s40747-022-00774-x","type":"journal-article","created":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T04:02:37Z","timestamp":1653710557000},"page":"5489-5510","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0726-1759","authenticated-orcid":false,"given":"Ana","family":"Aguilera","sequence":"first","affiliation":[]},{"given":"Raquel","family":"Pezoa","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Rodr\u00edguez-Delherbe","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"key":"774_CR1","unstructured":"Alvarez R, Cort\u00e9s J, Mattos-Arruda L, M.\u00a0F. et\u00a0al. 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