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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 26–50 of 1706 papers

TitleStatusHype
DIFER: Differentiable Automated Feature EngineeringCode1
A Survey of Information Cascade Analysis: Models, Predictions, and Recent AdvancesCode1
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability DetectionCode1
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code MatchingCode1
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTCode1
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate PredictionCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring SystemsCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using PhotoplethysmogramCode1
Can Models Help Us Create Better Models? Evaluating LLMs as Data ScientistsCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Anomaly Detection for Solder Joints Using β-VAECode1
CASPR: Customer Activity Sequence-based Prediction and RepresentationCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global InferenceCode1
Deep & Cross Network for Ad Click PredictionsCode1
Deep Dive into Hunting for LotLs Using Machine Learning and Feature Engineering.Code1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch LiteratureCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified