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 131–140 of 1706 papers

TitleStatusHype
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability DetectionCode1
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNsCode1
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
Pushing the boundaries of molecular property prediction for drug discovery with multitask learning BERT enhanced by SMILES enumerationCode1
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Deep Learning-Based Automatic Downbeat Tracking: A Brief ReviewCode0
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait RecognitionCode0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
DeepInf: Social Influence Prediction with Deep LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified