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 551–575 of 1706 papers

TitleStatusHype
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
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral EnhancementCode0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Augmenting data-driven models for energy systems through feature engineering: A Python framework for feature engineering—0
xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactionsCode0
Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques—0
Toward Efficient Automated Feature Engineering—0
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight ConditionsCode0
Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning—0
Improving Warped Planar Object Detection Network For Automatic License Plate Recognition—0
Tool flank wear prediction using high-frequency machine data from industrial edge device—0
ML-powered KQI estimation for XR services. A case study on 360-Video—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified