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 1291–1300 of 1706 papers

TitleStatusHype
A Dual-Layer Semantic Role Labeling System—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction—0
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs—0
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction—0
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Adversarial training for tabular data with attack propagation—0
AEFE: Automatic Embedded Feature Engineering for Categorical Features—0
A Feature Engineering Approach for Literary and Colloquial Tamil Speech Classification using 1D-CNN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified