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Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. Focus should be shifted from classic classification metrics towards per-class metrics (like AUC or Sensitivity) and rank agreement metrics (like Cohen\u2019s Kappa or Spearman\u2019s rank correlation coefficient). We present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC). We aim to show experimentally, using four different CNN architectures and two ordinal classification datasets, that the OBD+ECOC methodology significantly improves the mean results on the relevant ordinal and class-balancing metrics. The proposed method is able to outperform a nominal approach as well as already existing ordinal approaches, achieving a mean performance of <jats:inline-formula><jats:alternatives><jats:tex-math>$${{\\,\\mathrm{\\textit{RMSE}}\\,}}= 1.0797$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mrow>\n                      <mml:mspace\/>\n                      <mml:mi>RMSE<\/mml:mi>\n                      <mml:mspace\/>\n                    <\/mml:mrow>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mn>1.0797<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for the Retinopathy dataset and <jats:inline-formula><jats:alternatives><jats:tex-math>$${{\\,\\mathrm{\\textit{RMSE}}\\,}}= 1.1237$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mrow>\n                      <mml:mspace\/>\n                      <mml:mi>RMSE<\/mml:mi>\n                      <mml:mspace\/>\n                    <\/mml:mrow>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mn>1.1237<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for the Adience dataset averaged over 4 different architectures.\n<\/jats:p>","DOI":"10.1007\/s11063-022-10824-7","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T09:05:25Z","timestamp":1652346325000},"page":"5299-5330","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Error-Correcting Output Codes in the Framework of Deep Ordinal Classification"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9317-1428","authenticated-orcid":false,"given":"Javier","family":"Barbero-G\u00f3mez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2657-776X","authenticated-orcid":false,"given":"Pedro Antonio","family":"Guti\u00e9rrez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4564-1816","authenticated-orcid":false,"given":"C\u00e9sar","family":"Herv\u00e1s-Mart\u00ednez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"10824_CR1","doi-asserted-by":"crossref","unstructured":"Agresti A (2010) Analysis of ordinal categorical data. 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