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 561–570 of 1706 papers

TitleStatusHype
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified