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 301–325 of 1706 papers

TitleStatusHype
Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations—0
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores—0
Novel Representation Learning Technique using Graphs for Performance Analytics—0
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection—0
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness—0
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection—0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Universal Time-Series Representation Learning: A SurveyCode3
LLbezpeky: Leveraging Large Language Models for Vulnerability Detection—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network—0
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change—0
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting—0
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect SegmentationCode1
Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning—0
USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification—0
Morphological Profiling for Drug Discovery in the Era of Deep Learning—0
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning—0
Keyword spotting -- Detecting commands in speech using deep learning—0
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases—0
Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural NetworksCode0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
LiDAR-based Outdoor Crowd Management for Smart Campus on the Edge—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified