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 76–100 of 1706 papers

TitleStatusHype
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code MatchingCode1
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Deep Dive into Hunting for LotLs Using Machine Learning and Feature Engineering.Code1
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Benchmarks and Custom Package for Energy ForecastingCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor FactorizationCode1
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability DetectionCode1
DiviK: Divisive intelligent K-Means for hands-free unsupervised clustering in big biological dataCode1
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect SegmentationCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
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
A Survey of Information Cascade Analysis: Models, Predictions, and Recent AdvancesCode1
AutoGL: A Library for Automated Graph LearningCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch LiteratureCode1
General-Purpose User Embeddings based on Mobile App UsageCode1
Generative Pre-Training from MoleculesCode1
Understanding the Dynamics of DNNs Using Graph ModularityCode1
Discovering Neural WiringsCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
Anomaly Detection for Solder Joints Using β-VAECode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified