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 126–150 of 1706 papers

TitleStatusHype
End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using WearablesCode1
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object DetectionCode1
Automated Website Fingerprinting through Deep LearningCode1
Deep & Cross Network for Ad Click PredictionsCode1
Transfer Learning for Sequence Tagging with Hierarchical Recurrent NetworksCode1
DeepFM: A Factorization-Machine based Neural Network for CTR PredictionCode1
Unsupervised, Efficient and Semantic Expertise RetrievalCode1
Modelling Context with User Embeddings for Sarcasm Detection in Social MediaCode1
Wide & Deep Learning for Recommender SystemsCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction—0
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
Tabular Feature Discovery With Reasoning Type Exploration—0
A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners—0
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures—0
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
Feature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition—0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning—0
Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer—0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified