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 1551–1575 of 1706 papers

TitleStatusHype
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text ScoringCode0
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognitionCode0
Evaluating approaches for supervised semantic labelingCode0
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy TranscriptsCode0
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product ReviewsCode0
Iterative Feature Boosting for Explainable Speech Emotion RecognitionCode0
Event Detection and Domain Adaptation with Convolutional Neural NetworksCode0
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty DetectionCode0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
Activation Analysis of a Byte-Based Deep Neural Network for Malware ClassificationCode0
Joint RNN Model for Argument Component Boundary DetectionCode0
EvoAAA: An evolutionary methodology for automated autoencoder architecture searchCode0
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task LearningCode0
Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in ScotlandCode0
Template-based Question Answering using Recursive Neural NetworksCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement LearningCode0
Neural Ranking Models for Temporal Dependency Structure ParsingCode0
SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition SystemsCode0
Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health ClassificationCode0
Neural Sentiment Classification with User and Product AttentionCode0
Explainable Representation Learning of Small Quantum StatesCode0
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender SystemsCode0
CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record EmbeddingsCode0
LAC : LSTM AUTOENCODER with Community for Insider Threat DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified