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 601–650 of 1706 papers

TitleStatusHype
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems—0
Estimating Brain Age with Global and Local Dependencies—0
Self-Optimizing Feature Transformation—0
Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach—0
Tackling Data Drift with Adversarial Validation: An Application for German Text Complexity Estimation—0
Everybody likes short sentences - A Data Analysis for the Text Complexity DE Challenge 2022—0
Artificial Neural Networks for Finger Vein Recognition: A Survey—0
Lateral Movement Detection Using User Behavioral Analysis—0
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death—0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
Pseudo-Labels Are All You Need—0
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation—0
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution—0
Efficient Novelty Detection Methods for Early Warning of Potential Fatal DiseasesCode0
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network—0
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation—0
MACFE: A Meta-learning and Causality Based Feature Engineering FrameworkCode0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling—0
Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language—0
Few-shot incremental learning in the context of solar cell quality inspection—0
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection—0
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation—0
Using Person Embedding to Enrich Features and Data Augmentation for Classification—0
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set—0
Vibration fault detection in wind turbines based on normal behaviour models without feature engineering—0
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach—0
Human heuristics for AI-generated language are flawed—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
UMUTextStats: A linguistic feature extraction tool for Spanish—0
Towards Context-Aware Neural Performance-Score Synchronisation—0
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
A Hybrid Model for Forecasting Short-Term Electricity Demand—0
Learning latent representations for operational nitrogen response rate prediction—0
A Survey on Semantics in Automated Data Science—0
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning—0
TaDeR: A New Task Dependency Recommendation for Project Management Platform—0
A simple framework for contrastive learning phases of matter—0
On Designing Data Models for Energy Feature Stores—0
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor NetworksCode0
Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in ScotlandCode0
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified