Self-organizing maps with supervised layer

We present in this paper a new approach of supervised self organizing map (SOM). We added a supervised perceptron layer to the classical SOM approach. This combination allows the classification of new patterns by taking into account all the map prototypes without changing the SOM organization. We also propose to associate two reject options to our supervised SOM. This allows to improve the results reliability and to discover new classes in applications where some classes are unknown. We obtain two variants of supervised SOM with rejection that have been evaluated on different datasets. The results indicate that our approaches are competitive with most popular supervised leaning algorithms like support vector machines and random forest.