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 176–200 of 1706 papers

TitleStatusHype
Reinforcement Feature Transformation for Polymer Property Performance Prediction—0
A Feature Engineering Approach for Literary and Colloquial Tamil Speech Classification using 1D-CNN—0
Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks—0
Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety—0
Leveraging Open-Source Large Language Models for Native Language Identification—0
MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous DrivingCode2
Learn2Aggregate: Supervised Generation of Chvátal-Gomory Cuts Using Graph Neural Networks—0
HybridFC: A Hybrid Fact-Checking Approach for Knowledge GraphsCode0
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration—0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features—0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
Application Research On Real-Time Perception Of Device Performance Status—0
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
Hybridization of Persistent Homology with Neural Networks for Time-Series Prediction: A Case Study in Wave Height—0
PoliPrompt: A High-Performance Cost-Effective LLM-Based Text Classification Framework for Political Science—0
LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition—0
Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach—0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning MethodCode0
Obfuscated Memory Malware Detection—0
Improving Radiography Machine Learning Workflows via Metadata Management for Training Data Selection—0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
Transfer Learning and the Early Estimation of Single-Photon Source Quality using Machine Learning MethodsCode0
Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified