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 1426–1450 of 1706 papers

TitleStatusHype
DeepInf: Social Influence Prediction with Deep LearningCode0
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries ChallengeCode0
CharNER: Character-Level Named Entity RecognitionCode0
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait RecognitionCode0
Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression LearningCode0
Hyperbolic Representation Learning for Fast and Efficient Neural Question AnsweringCode0
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient DetectionCode0
Disfluency Detection using Auto-Correlational Neural NetworksCode0
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural NetworksCode0
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG SignalsCode0
A Neurochaos Learning Architecture for Genome ClassificationCode0
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR PredictionCode0
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral FeaturesCode0
Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based ModelsCode0
Weakly-Supervised Neural Text ClassificationCode0
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in textCode0
ML-Net: multi-label classification of biomedical texts with deep neural networksCode0
Dominant motion identification of multi-particle system using deep learning from videoCode0
Identifying Quantum Phase Transitions with Adversarial Neural NetworksCode0
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News ClassificationCode0
User Intent Prediction in Information-seeking ConversationsCode0
Advancing Automated Deception Detection: A Multimodal Approach to Feature Extraction and AnalysisCode0
Syntax for Semantic Role Labeling, To Be, Or Not To BeCode0
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction PredictionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified