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 51–75 of 1706 papers

TitleStatusHype
End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using WearablesCode1
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge DevicesCode1
CASPR: Customer Activity Sequence-based Prediction and RepresentationCode1
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Feature Programming for Multivariate Time Series PredictionCode1
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
Graph Contrastive Learning for Anomaly DetectionCode1
BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using PhotoplethysmogramCode1
Understanding the Dynamics of DNNs Using Graph ModularityCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Automated Website Fingerprinting through Deep LearningCode1
AutoML: A Survey of the State-of-the-ArtCode1
Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking SequencesCode1
A Survey of Information Cascade Analysis: Models, Predictions, and Recent AdvancesCode1
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring SystemsCode1
Can Models Help Us Create Better Models? Evaluating LLMs as Data ScientistsCode1
Anomaly Detection for Solder Joints Using β-VAECode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified