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 611–620 of 1706 papers

TitleStatusHype
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
Pseudo-Labels Are All You Need—0
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation—0
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution—0
Efficient Novelty Detection Methods for Early Warning of Potential Fatal DiseasesCode0
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified