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 451–475 of 1706 papers

TitleStatusHype
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering—0
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code—0
Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase—0
IoT Device Identification Based on Network Communication Analysis Using Deep Learning—0
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches—0
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product ReviewsCode0
Streamlining models with explanations in the learning loopCode0
Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars—0
Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning—0
Scientific Computing with Diffractive Optical Neural Networks—0
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends—0
Stop overkilling simple tasks with black-box models and use transparent models insteadCode0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection—0
Mnemosyne: Learning to Train Transformers with Transformers—0
An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation—0
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation PoliciesCode0
G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P NetworkCode0
Data-driven intelligent computational design for products: Method, techniques, and applications—0
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A ReviewCode0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation—0
The Recent Advances in Automatic Term Extraction: A survey—0
EvoAAA: An evolutionary methodology for automated autoencoder architecture searchCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified