The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Machine learning on small size samples: A synthetic knowledge synthesis
Open accessAbstract
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.
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Cites
Software survey: VOSviewer, a computer program for bibliometric mapping
van Eck, N. J., & Waltman, L. (2010) · Scientometrics · doi:10.1007/s11192-009-0146-3 · Cited by 21,484
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682
Visualizing Bibliometric Networks
van Eck, N. J., & Waltman, L. (2014) · Measuring Scholarly Impact · doi:10.1007/978-3-319-10377-8_13 · Cited by 3,342
The role of google scholar in evidence reviews and its applicability to grey literature searching
Haddaway, N. R., Collins, A. M., Coughlin, D., & Kirk, S. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138237 · Cited by 2,387
Machine learning algorithm validation with a limited sample size
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224365 · Cited by 1,901
Statistical Strategies for Small Sample Research
Hoyle, R. H. (1999) · Cited by 1,896
Abstraction and interpretation during the qualitative content analysis process
Lindgren, B.-M., Lundman, B., & Graneheim, U. H. (2020) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2020.103632 · Cited by 1,414
Deep convolutional neural network based medical image classification for disease diagnosis
Yadav, S. S., & Jadhav, S. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0276-2 · Cited by 1,069
Very deep convolutional neural network based image classification using small training sample size
Liu, S., & Deng, W. (2015) · 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2015.7486599 · Cited by 1,056
Cited by
Raza, M. L., Hassan, S. T., Jamil, S., Hyder, N., Batool, K., Walji, S., & Abbas, M. K. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1557177 · Cited by 74
Blanco, K., Salcidua, S., Orellana, P., Sauma-Pérez, T., León, T., Steinmetz, L. C. L., Ibañez, A., Duran-Aniotz, C., & de la Cruz, R. (2023) · Alzheimer's Research & Therapy · doi:10.1186/s13195-023-01304-8 · Cited by 73
Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis
Sun, T.-H., Wang, C.-C., Wu, Y.-L., Hsu, K.-C., & Lee, T.-H. (2023) · Scientific Reports · doi:10.1038/s41598-023-42338-0 · Cited by 32
Metadata
- Authors
- Kokol, P., Kokol, M., & Zagoranski, S.
- Venue
- Science Progress
- Publisher
- SAGE Publications
- Published
- 2022-01
- Volume
- 105
- Issue
- 1
- Pages
- 368504211029777-368504211029777
- ISSN
- 0036-8504 (ISSN-L)
- 2047-7163
- OpenAlex
- W3135775894
- Cited by
- 258
- References
- 67
- FWCI
- 16.88
- Type
- journal article
- Language
- en
- Open access
- gold
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- Kokol, P., Kokol, M., & Zagoranski, S. (2022). Machine learning on small size samples: A synthetic knowledge synthesis. Science Progress. https://doi.org/10.1177/00368504211029777
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
