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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 1081–1090 of 1706 papers

TitleStatusHype
Multimodal Speech Emotion Recognition and Ambiguity ResolutionCode0
Feature Engineering for Mid-Price Prediction with Deep Learning—0
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement LearningCode0
A Graph-based Model for Joint Chinese Word Segmentation and Dependency ParsingCode0
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworksCode0
On the Vulnerability of CNN Classifiers in EEG-Based BCIs—0
The Landscape of R Packages for Automated Exploratory Data AnalysisCode0
Activation Analysis of a Byte-Based Deep Neural Network for Malware ClassificationCode0
SAFE ML: Surrogate Assisted Feature Extraction for Model LearningCode0
Field-aware Neural Factorization Machine for Click-Through Rate Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified