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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AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
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.
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.
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.
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.
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.
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.
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.