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 791–800 of 1706 papers

TitleStatusHype
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection—0
Flexible Operator Embeddings via Deep Learning—0
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework—0
Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach—0
Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
An Efficient Architecture for Predicting the Case of Characters using Sequence Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified