Machine learning on small size samples: A synthetic knowledge synthesis

Machine Learning is an increasingly important technology dealing with the growing complexity of the digitalised world. 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 still faced with small data samples. The present bibliometric knowledge synthesis study aims to answer the research question 'What is the small data problem in machine learning and how it is solved?' The analysis a positive trend in the number of research publications and substantial growth of the research community, indicating that the research field is reaching maturity. Most productive countries are China, United States and United Kingdom. Despite notable international cooperation, the regional concentration of research literature production in economically more developed countries was observed. Thematic analysis identified four research themes. The themes are concerned with to dimension reduction in complex big data analysis, data augmentation techniques in deep learning, data mining and statistical learning on small datasets.

Automated summary

This article presents a bibliometric knowledge synthesis study on the small data problem in machine learning and its solutions. The study reveals a positive trend in research publications and the growth of the research community, indicating the maturity of the field. China, the United States, and the United Kingdom are identified as the most productive countries. The study also identifies four research themes, including dimension reduction, data augmentation techniques, data mining, and statistical learning on small datasets.