Automated Feature Engineering
22 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.
Benchmarks
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Most implemented 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.
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.
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018.
Supervised Video Summarization via Multiple Feature Sets with Parallel Attention
TIBHannover/MSVA
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The proposed architecture utilizes an attention mechanism before fusing motion features and features representing the (static) visual content, i. e., derived from an image classification model.
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms.
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.
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.
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.
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.
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.
