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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 751–775 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
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report—0
Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions—0
Feature Engineering on LMS Data to Optimize Student Performance Prediction—0
Feature Engineering vs BERT on Twitter Data—0
Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature—0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model—0
Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data—0
Feature-Enriched Character-Level Convolutions for Text Regression—0
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches—0
FeatureForge: A Novel Tool for Visually Supported Feature Engineering and Corpus Revision—0
Feature Imitating Networks—0
Feature Optimization for Constituent Parsing via Neural Networks—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