Exploiting Ordinal Class Structure in Multiclass Classification: Application to Ovarian Cancer

Abstract

In multiclass machine learning problems, one needs to distinguish between the nominal labels that do not have any natural ordering and the ordinal labels that are ordered. Ordinal labels are pervasive in biology, and some examples are given here. In this note, we point out the importance of making use of the order information when it is inherent to the problem. We demonstrate that algorithms that use this additional information outperform the algorithms that do not, on a case study of assigning one of four labels to the ovarian cancer patients on the basis of their time of progression-free survival. As an aside, it is also pointed out that the algorithms that make use of ordering information require fewer data normalizations. This aspect is important in biological applications, where data are plagued by variations in platforms and protocols, batch effects, and so on.