Abstract
In this paper, we propose a supervised self-organizing maps algorithm, named OrderSOM, which updates parameters using instance pairs with the relative order labels and then predicts the rank of unseen instances. Order SOM achieves order learning by adjusting the Best Matching Unit of the input instance based on its order labels. Through experiments conducted on synthetic dataset and real dataset, the effectiveness of OrderSOM was verified.
