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 401–425 of 1706 papers

TitleStatusHype
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships—0
Making forecasting self-learning and adaptive -- Pilot forecasting rack—0
Feature Programming for Multivariate Time Series PredictionCode1
Explainable Representation Learning of Small Quantum StatesCode0
How Can Recommender Systems Benefit from Large Language Models: A SurveyCode3
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Unified Embedding Based Personalized Retrieval in Etsy Search—0
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering ApproachCode0
A Hybrid Approach for Smart Alert Generation—0
Fair multilingual vandalism detection system for WikipediaCode0
Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks—0
Introduction to Medical Imaging Informatics—0
Managed Geo-Distributed Feature Store: Architecture and System Design—0
DUBLIN -- Document Understanding By Language-Image Network—0
Systematic Literature Review on Application of Machine Learning in Continuous Integration—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified