SOTAVerified

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 841–850 of 1706 papers

TitleStatusHype
Graph Neural Networks and Boolean Satisfiability—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction—0
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Artificial Intelligence for Diabetes Case Management: The Intersection of Physical and Mental Health—0
GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores—0
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified