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 951–975 of 1706 papers

TitleStatusHype
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
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
An Empirical Study of Factors Affecting Language-Independent Models—0
AraNet: A Deep Learning Toolkit for Arabic Social MediaCode0
A Gated Recurrent Unit Approach to Bitcoin Price Prediction—0
Customers Churn Prediction in Financial Institution Using Artificial Neural Network—0
Extraction of Heart Rate from PPG Signal: A Machine Learning Approach using Decision Tree Regression Algorithm—0
Random CapsNet Forest Model for Imbalanced Malware Type Classification Task—0
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable BytesCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Predictive Precompute with Recurrent Neural Networks—0
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates—0
Feature Engineering Combined with 1 D Convolutional Neural Network for Improved Mortality Prediction—0
Integrating Deep Learning with Logic Fusion for Information Extraction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified