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.
Highlights
Machine learning, dating back to the 1950s, from the discovery of perceptron to the present day, has been deeply studied by many researchers and developed much more complex methods and many of them used in the different fields
Some physical properties of different coins used in Anatolian geography were collected and classified by various machine learning methods named Support vector machines (SVM), Random Forest, Bagging, and Decision Trees
This study examined whether machine learning methods can determine the periods of historical coins
Summary
Machine learning, dating back to the 1950s, from the discovery of perceptron to the present day, has been deeply studied by many researchers and developed much more complex methods and many of them used in the different fields. When we look at the working mechanisms of machine learning methods, what we will see is that a machine can solve very complex problems in a much shorter time when the human mind will be inadequate. As this fact may have some drawbacks (e.g. reduction of mankind's labor need in the future), there are some advantages (e.g. the ability of machines to diagnose before and more accurately than doctors). From a more technical point of view, we can aggregate machine learning methods in roughly three topics: Supervised Learning, Unsupervised learning, and Semi-Supervised learning To understand these concepts more deeply, we need to look at what data means. If the correct output of some data samples is known, some are unknown, semi-supervised methods can be used
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