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 976–1000 of 1706 papers

TitleStatusHype
RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction—0
Random CapsNet Forest Model for Imbalanced Malware Type Classification Task—0
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem—0
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning—0
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks—0
RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition—0
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems—0
Real-Time Well Log Prediction From Drilling Data Using Deep Learning—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Recognizing irregular entities in biomedical text via deep neural networks—0
Recognizing Salient Entities in Shopping Queries—0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
Recurrent Convolutional Neural Networks for Discourse Compositionality—0
Recurrent neural network models for disease name recognition using domain invariant features—0
Recurrent Neural Networks for Time Series Forecasting—0
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Regularized Structured Perceptron: A Case Study on Chinese Word Segmentation, POS Tagging and Parsing—0
Reinforcement Feature Transformation for Polymer Property Performance Prediction—0
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures—0
Relation extraction from clinical texts using domain invariant convolutional neural network—0
Relation Extraction: Perspective from Convolutional Neural Networks—0
Rep\'erage des entit\'es nomm\'ees pour l'arabe : adaptation non-supervis\'ee et combinaison de syst\`emes (Named Entity Recognition for Arabic : Unsupervised adaptation and Systems combination) [in French]—0
Representation Learning for Aspect Category Detection in Online Reviews—0
Residual Attention Based Network for Automatic Classification of Phonation Modes—0
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified