| Title | Ordinal Prototype-Based Classifiers |
| Authors | Burkovski, Andre, Schirra, Lyn-Rouven, Schmid, Florian, Lausser, Ludwig and Kestler, Hans A. |
| Year | 2017 |
| Volume | Archives of Data Science, Series A 2(2) / 2017 |
| Abstract | The identification of prototypical patterns is one of the major goals in the classification of microarray data. Prototype-based classifiers are of special interest in this context, since they allow a direct biological interpretation. In this work we present prototype-based classifiers that rely on ordinal-scaled data. Advantage of these ordinal-scaled signatures is their invariance to a wide range of data transformations. Standard prototype-based classifiers can be modified to this type of data by utilizing rank-distances and rank-aggregation procedures. In this study, we compare the proposed methods with standard classifiers. They are examined in experiments with and without feature selection on a panel of publicly available microarray datasets. We show that the proposed techniques result in the construction of different signatures that improve classification performance. |