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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 251–260 of 1706 papers

TitleStatusHype
Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry—0
Automatic Seizure Prediction using CNN and LSTM—0
Automation of Feature Engineering for IoT Analytics—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
autoNLP: NLP Feature Recommendations for Text Analytics Applications—0
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs—0
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India—0
A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks—0
Applications of Large Language Model Reasoning in Feature Generation—0
Application Research On Real-Time Perception Of Device Performance Status—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified