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 451–475 of 1706 papers

TitleStatusHype
Cross-lingual Knowledge Graph Alignment via Graph Convolutional NetworksCode0
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognitionCode0
Deep Affix Features Improve Neural Named Entity RecognizersCode0
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency ParsingCode0
A Two Dimensional Feature Engineering Method for Relation ExtractionCode0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
DeepAtom: A Framework for Protein-Ligand Binding Affinity PredictionCode0
CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social MediaCode0
Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health RecordsCode0
Danish Stance Classification and Rumour ResolutionCode0
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News ClassificationCode0
Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMsCode0
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering ApproachCode0
Towards Wide Learning: Experiments in HealthcareCode0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning—0
Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry—0
Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction—0
Attention-Based Convolutional Neural Network for Machine Comprehension—0
A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction—0
Machine Learning for Wireless Link Quality Estimation: A Survey—0
A Time-Frequency based Suspicious Activity Detection for Anti-Money Laundering—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified