Machine learning on small size samples: A synthetic knowledge synthesis

TL;DR

This paper reviews the challenges of applying machine learning to small datasets, highlighting recent research trends, solutions, and the growing academic focus despite regional disparities.

Contribution

It synthesizes current knowledge on small data problems in machine learning and analyzes research trends and regional distribution.

Findings

Increasing research publications on small datasets

Growing international collaboration in the field

Regional concentration in developed countries

Abstract

One of the increasingly important technologies dealing with the growing complexity of the digitalization of almost all human activities is Artificial intelligence, more precisely machine learning Despite the fact, that we live in a Big data world where almost everything is digitally stored, there are many real-world situations, where researchers are faced with small data samples. The present study aim is to answer the following research question namely What is the small data problem in machine learning and how it is solved?. Our bibliometric study showed a positive trend in the number of research publications concerning the use of small datasets and substantial growth of the research community dealing with the small dataset problem, indicating that the research field is moving toward higher maturity levels. Despite notable international cooperation, the regional concentration of research literature production in economically more developed countries was observed.