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 1376–1400 of 1706 papers

TitleStatusHype
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Deep Learning for Answer Sentence SelectionCode0
Classical Machine Learning Techniques in the Search of Extrasolar PlanetsCode0
Machine learning for complete intersection Calabi-Yau manifolds: a methodological studyCode0
What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtractionCode0
Machine Learning for K-adaptability in Two-stage Robust OptimizationCode0
Machine learning for predicting thermal power consumption of the Mars Express SpacecraftCode0
GSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media SteganalysisCode0
Guided Cost Learning: Deep Inverse Optimal Control via Policy OptimizationCode0
A Novel Approach to Radiometric IdentificationCode0
gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning MethodCode0
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided MaskingCode0
Precog-LTRC-IIITH at GermEval 2021: Ensembling Pre-Trained Language Models with Feature EngineeringCode0
Weakly-Supervised Hierarchical Text ClassificationCode0
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A ReviewCode0
Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health RecordsCode0
Machine Learning Pipelines with Modern Big Data Tools for High Energy PhysicsCode0
Machine Translation Evaluation using Recurrent Neural NetworksCode0
Self-regulation: Employing a Generative Adversarial Network to Improve Event DetectionCode0
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
deepQuest: A Framework for Neural-based Quality EstimationCode0
Malicious Network Traffic Detection via Deep Learning: An Information Theoretic ViewCode0
BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly ModelsCode0
Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment AnalysisCode0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified