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 401–450 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
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
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language ModelCode1
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
Bayesian Optimization of Catalysis With In-Context LearningCode1
Practical Lessons on Optimizing Sponsored Products in eCommerce—0
Classification of integers based on residue classes via modern deep learning algorithmsCode0
Modelling customer churn for the retail industry in a deep learning based sequential framework—0
AMC-Net: An Effective Network for Automatic Modulation Classification—0
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability DetectionCode1
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting—0
Improving extreme weather events detection with light-weight neural networks—0
Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions—0
Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization—0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes—0
Clustering US Counties to Find Patterns Related to the COVID-19 Pandemic—0
A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects—0
Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Deep Learning for Iris Recognition: A Review—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified