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 926–950 of 1706 papers

TitleStatusHype
Interleaved Sequence RNNs for Fraud Detection—0
Keyphrase Extraction with Span-based Feature Representations—0
Lifting Interpretability-Performance Trade-off via Automated Feature EngineeringCode0
Towards explainable meta-learning—0
autoNLP: NLP Feature Recommendations for Text Analytics Applications—0
Supervised Learning on Relational Databases with Graph Neural NetworksCode1
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning—0
Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model—0
A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning ApproachCode0
An Efficient Architecture for Predicting the Case of Characters using Sequence Models—0
Real-Time Well Log Prediction From Drilling Data Using Deep Learning—0
Print Defect Mapping with Semantic Segmentation—0
Are Accelerometers for Activity Recognition a Dead-end?—0
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
Knowledge-aware Attention Network for Protein-Protein Interaction ExtractionCode1
Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health ClassificationCode0
Temporal Tensor Transformation Network for Multivariate Time Series Prediction—0
Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis—0
Fourier Transform Approach to Machine Learning III: Fourier Classification—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions—0
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions—0
AraNet: A Deep Learning Toolkit for Arabic Social MediaCode0
An Empirical Study of Factors Affecting Language-Independent Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified