Automated Feature Engineering
17 papers with code • 0 benchmarks • 0 datasets
Automated feature engineering improves upon the traditional approach to feature engineering by automatically extracting useful and meaningful features from a set of related data tables with a framework that can be applied to any problem.
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Latest papers
Benchmarking Automatic Machine Learning Frameworks
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
Layered TPOT: Speeding up Tree-based Pipeline Optimization
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
AutoLearn - Automated Feature Generation and Selection
In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications.
Solving the "false positives" problem in fraud prediction
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.
ExploreKit: Automatic Feature Generation and Selection
To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features.
Deep Feature Synthesis: Towards Automating Data Science Endeavors
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.
