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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 751–760 of 1706 papers

TitleStatusHype
Feature Engineering Combined with 1 D Convolutional Neural Network for Improved Mortality Prediction—0
Feature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring—0
Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks—0
Feature Engineering for Knowledge Base Construction—0
Feature Engineering for Map Matching of Low-Sampling-Rate GPS Trajectories in Road Network—0
Feature Engineering for Mid-Price Prediction with Deep Learning—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Feature Engineering for Scalable Application-Level Post-Silicon Debugging—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified