Feature Engineering for Machine Learning
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 3648
Special Issue Editor
Special Issue Information
Dear Colleagues,
Features are base material in the building of a learning model. At the same time, they are the most important factor affecting the performance of a learning model. Thus, we cannot think of machine learning without feature engineering. There is no golden rule for feature engineering; even though there are lots of research works, we still need a more efficient methodology to find effective features for a specific learning model.
This Special Issue on “Feature Engineering for Machine Learning” aims to present recent research related to feature engineering and give insight for building high-performance learning models. Submissions are expected to focus on both the theoretical aspects and applications of feature engineering. Review papers are also welcome.
Topics of interest include but are not limited to the following areas:
- Feature generation/selection/extraction
- Feature synthesis
- Dimension reduction
- Feature importance in a learning model
- Feature interaction
- Visualization of a feature-related task
- Automation of feature engineering
- Development of a feature engineering tool
I hope this Special Issue works as a roadmap for all researchers of feature engineering and developers of learning models.
Prof. Dr. Sejong Oh
Guest Editor
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