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 421–430 of 1706 papers

TitleStatusHype
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets—0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation—0
Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency—0
Deep Dive into Hunting for LotLs Using Machine Learning and Feature Engineering.Code1
Schooling to Exploit Foolish Contracts—0
Smart Learning to Find Dumb Contracts (Extended Version)—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified