https://www.springer.com/journal/10994/updates/17274390Date of Submission: March 1st, 2020
First review round: June 31st, 2020
Revision: October 1st, 2020
Final decision: December 30th, 2020
The main objective of machine learning is to extract patterns to turn data into knowledge. Since the beginning of this century, technological advances have drastically changed the size of data sets as well as the speed with which these data must be analyzed. Modern data sets may have a huge number of instances, a very large number of features, or both. In most applications, data sets are compiled by combining data from different sources and databases (containing both structured and unstructured data) where each source of information has its strengths and weaknesses. Before applying any machine learning algorithm, it is therefore necessary to create interesting features from the data sources. This essential step, which is denoted “feature engineering” or feature extraction, is of utmost importance in the machine learning process. Machine learners should be well aware of the power of feature engineering and it is important to share good practices.
This special issue aims to bring together innovative feature engineering techniques and/or successful development of features that improve the performance and/or interpretability of machine learning models. Both manual (relying on human creativity and/or domain knowledge) as well as automated (obtained for example from a relational dataset) feature engineering techniques are considered. We encourage novel featurization techniques that are based on diverse and alternative data sources and that leverage the temporal, granular, or unstructured aspects of the data.
