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 911–920 of 1706 papers

TitleStatusHype
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Optimizing a PoS Tagset for Norwegian Dependency Parsing—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition—0
OSPC: Artificial VLM Features for Hateful Meme Detection—0
PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals—0
Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection—0
Parsing with Compositional Vector Grammars—0
Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data—0
Patient Cohort Retrieval using Transformer Language Models—0
Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified