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 751–775 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
MalNet: A Large-Scale Image Database of Malicious SoftwareCode1
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
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNsCode1
The Challenges of Persian User-generated Textual Content: A Machine Learning-Based ApproachCode1
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
Summaformers @ LaySumm 20, LongSumm 20Code1
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields—0
Simplified DOM Trees for Transferable Attribute Extraction from the WebCode1
Statistical learning for accurate and interpretable battery lifetime predictionCode1
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
A Numbers Game: Numeric Encoding Options with Automunge—0
String Theory: Parsed Categoric Encodings with Automunge—0
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
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified