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 801–825 of 1706 papers

TitleStatusHype
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review—0
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition—0
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures—0
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey—0
Machine Learning in LiDAR 3D point clouds—0
Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data—0
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG—0
MONAH: Multi-Modal Narratives for Humans to analyze conversationsCode0
A Survey on Extraction of Causal Relations from Natural Language Text—0
Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning ApproachCode0
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields—0
Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning—0
Detecting Singleton Spams in Reviews via Learning Deep Anomalous Temporal Aspect-Sentiment PatternsCode0
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis—0
Simple deductive reasoning tests and data sets for exposing limitation of today's deep neural networks—0
String Theory: Parsed Categoric Encodings with Automunge—0
A Numbers Game: Numeric Encoding Options with Automunge—0
Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attention—0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Shape-based Feature Engineering for Solar Flare Prediction—0
Explainable Multi-class Classification of Medical Data—0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Intelligent Vector-based Customer Segmentation in the Banking Industry—0
Unboxing Engagement in YouTube Influencer Videos: An Attention-Based Approach—0
Machine Learning for Detecting Data Exfiltration: A Review—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified