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
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
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media DataCode0
Classical Machine Learning Techniques in the Search of Extrasolar PlanetsCode0
DeepAtom: A Framework for Protein-Ligand Binding Affinity PredictionCode0
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted FeaturesCode0
Projective Quadratic Regression for Online Learning—0
Cross-Class Relevance Learning for Temporal Concept Localization—0
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data—0
Learning based Methods for Code Runtime Complexity Prediction—0
Semi-Supervised Semantic Role Labeling with Cross-View Training—0
A System for Diacritizing Four Varieties of Arabic—0
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News ClassificationCode0
A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified