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 426–450 of 1706 papers

TitleStatusHype
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