TY - JOUR A1 - Bellmann, Peter A1 - Lausser, Ludwig A1 - Kestler, Hans A. A1 - Schwenker, Friedhelm T1 - A Theoretical Approach to Ordinal Classification BT - Feature Space-Based Definition and Classifier-Independent Detection of Ordinal Class Structures JF - Applied Sciences N2 - Ordinal classification (OC) is a sub-discipline of multi-class classification (i.e., including at least three classes), in which the classes constitute an ordinal structure. Applications of ordinal classification can be found, for instance, in the medical field, e.g., with the class labels order, early stage-intermediate stage-final stage, corresponding to the task of classifying different stages of a certain disease. While the field of OC was continuously enhanced, e.g., by designing and adapting appropriate classification models as well as performance metrics, there is still a lack of a common mathematical definition for OC tasks. More precisely, in general, a classification task is defined as an OC task, solely based on the corresponding class label names. However, an ordinal class structure that is identified based on the class labels is not necessarily reflected in the corresponding feature space. In contrast, naturally any kind of multi-class classification task can consist of a set of arbitrary class labels that form an ordinal structure which can be observed in the current feature space. Based on this simple observation, in this work, we present our generalised approach towards an intuitive working definition for OC tasks, which is based on the corresponding feature space and allows a classifier-independent detection of ordinal class structures. To this end, we introduce and discuss novel, OC-specific theoretical concepts. Moreover, we validate our proposed working definition in combination with a set of traditionally ordinal and traditionally non-ordinal data sets, and provide the results of the corresponding detection algorithm. Additionally, we motivate our theoretical concepts, based on an illustrative evaluation of one of the oldest and most popular machine learning data sets, i.e., on the traditionally non-ordinal Fisher’s Iris data set. UR - https://doi.org/10.3390/app12041815 KW - ordinal classification KW - detection of ordinal class structures KW - Fisher’s discriminant ratio Y1 - 2022 UR - https://doi.org/10.3390/app12041815 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27186 SN - 2076-3417 N1 - This article belongs to the Section "Computing and Artificial Intelligence" VL - 12 (2022) IS - 4 PB - MDPI CY - Basel ER - TY - CHAP A1 - Lausser, Ludwig A1 - Szekely, Robin A1 - Kessler, Viktor A1 - Schwenker, Friedhelm ED - Pancioni, Luca ED - Schwenker, Friedhelm ED - Trentin, Edmondo T1 - Selecting Features from Foreign Classes T2 - Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19–21, 2018: Proceedings UR - https://doi.org/10.1007/978-3-319-99978-4_5 Y1 - 2018 UR - https://doi.org/10.1007/978-3-319-99978-4_5 SN - 978-3-319-99978-4 SN - 978-3-319-99977-7 SP - 66 EP - 77 PB - Springer CY - Cham ER - TY - CHAP A1 - Bellmann, Peter A1 - Lausser, Ludwig A1 - Kestler, Hans A. A1 - Schwenker, Friedhelm ED - Del Bimbo, Alberto ED - Cucchiara, Rita ED - Sclaroff, Stan ED - Farinella, Giovanni Maria ED - Mei, Tao ED - Bertini, Marco ED - Escalante, Hugo Jair ED - Vezzani, Roberto T1 - Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario T2 - Pattern Recognition: ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021: Proceedings, Part VI UR - https://doi.org/10.1007/978-3-030-68780-9_58 KW - Ordinal classifier cascades KW - Ordinal classification KW - Pain intensity recognition KW - Physiological signals Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-68780-9_58 SN - 978-3-030-68780-9 SN - 978-3-030-68779-3 SP - 773 EP - 787 PB - Springer CY - Cham ER -