Classification of Historical Anatolian Coins with Machine Learning Algorithms

Abstract

To find out which period the historical coins belong to requires a number of scientific procedures that archaeologists or experts can do. These operations can often be time-consuming and demanding operations. From this point on, in this study, the automatically classification of historical coins by using machine learning methods is discussed. Being able to use machine learning methods to classify historical coins can help experts and can become an analysis tool without the need for scientific tests for non-experts. For this purpose, some physical properties of different coins used in Anatolian geography were collected and classified by various machine learning methods named SVM, Random Forest, Bagging, and Decision Trees. Also, two different missing values strategies are deployed in conjunction with each chosen method. Based on our findings, random forest method together with imputing missing values with mean gives an acceptable results with the accuracy rate of %71, although there are some limitations such as high rate of missing values and working with a small dataset.

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“…In Ünlü's study, support vector machine (SVM), RF, bagging and decision trees methods were applied to classify historical coins. RF showed a higher performance than other methods with 71% correct classification rate [8].…”