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Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 811–820 of 1706 papers

TitleStatusHype
Generalized Convolutional Neural Networks for Point Cloud Data—0
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis—0
Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation—0
Breast mass classification in ultrasound based on Kendall's shape manifold—0
An Unsupervised Model with Attention Autoencoders for Question Retrieval—0
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine—0
Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure—0
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
GeoDecoder: Empowering Multimodal Map Understanding—0
Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified