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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 381–390 of 1706 papers

TitleStatusHype
Sparse Array Design for Direction Finding using Deep Learning—0
Deep Feature Learning for Wireless Spectrum Data—0
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion—0
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate PredictionCode1
Multi-output Headed Ensembles for Product Item Classification—0
Adversarial training for tabular data with attack propagation—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
Alzheimer's Disease Detection from Spontaneous Speech and Text: A review—0
TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual ExplanationsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified