EigenCoin: sassanid coins classification based on Bhattacharyya distance
Incremental improvement for domain-specific coin classification with imbalanced data.
The paper tackles imbalanced database classification for Sassanid coins, proposing EigenCoin with Bhattacharyya distance. It achieves accuracy improvements of 9.45% to 21.75% over other algorithms while handling over-fitting.
Solving pattern recognition problems using imbalanced databases is a hot topic, which entices researchers to bring it into focus. Therefore, we consider this problem in the application of Sassanid coins classification. Our focus is not only on proposing EigenCoin manifold with Bhattacharyya distance for the classification task, but also on testing the influence of the holistic and feature-based approaches. EigenCoin consists of three main steps namely manifold construction, mapping test data, and classification. Conducted experiments show EigenCoin outperformed other observed algorithms and achieved the accuracy from 9.45% up to 21.75%, while it has the capability of handling the over-fitting problem.