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 1001–1010 of 1706 papers

TitleStatusHype
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems—0
Feature Cross Search via Submodular Optimization—0
Feature Engineering and Classification Models for Partial Discharge in Power Transformers—0
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction—0
Feature Engineering Approach to Building Load Prediction: A Case Study for Commercial Building Chiller Plant Optimization in Tropical Weather—0
Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks—0
Feature Engineering Combined with 1 D Convolutional Neural Network for Improved Mortality Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified