The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
This provides a data-driven update to a 2007 qualitative survey, reducing bias and offering insights for researchers to guide their work, though it is incremental in methodology.
The study tackled the problem of identifying the most common topics in machine learning research by quantitatively analyzing 54,000 abstracts from 2007 to 2016, revealing the top 10 topics to help researchers identify popular and emerging areas.
Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research.