Published April 16, 2024 | Version v4
Conference paper Open

Creating an Additional Class Layer with Machine Learning to counter Overfitting in an Unbalanced Ancient Coin Dataset

  • 1. Goethe-University, Frankfurt am Main


We have implemented an approach based on Convolutional Neural Networks (CNN) for mint recognition for our Corpus Nummorum (CN) coin dataset as an alternative to coin type recognition, since we had too few instances for most of the types (classes). However, this shift increased an existing problem with our dataset: the extremely unbalaced number of instances per class. While some of our classes consist of only 20 instances, others consist of several hundred. After training our VGG16 model we unsurprisingly observed an overfitting of these “big” classes within the confusion matrix. To reduce this problem, we tried to split the classes with the most images into several smaller ones and called them additional class layers. We use three different machine learning (ML) approaches to perform this breakdown. One is an unsupervised clustering method without additional manual work. The other two are supervised approaches taking into account the motifs of the coins themselves: a) an object detection model that predicts trained entities, and b) a Natural Language Processing (NLP) method to find entities in the textual descriptions of the coins. Based on the combination of obverse and reverse results from these two approaches the new additional class layer were defined. After retraining of our mint recogntion model with these new classes, we evaluated the results based on the confusion matrix. In our case, the best results could be observed by forming additional class layer based on the NLP method.



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Dataset: 10.5281/zenodo.10033993 (DOI)