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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
F-RBA: A Federated Learning-based Framework for Risk-based Authentication—0
Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health RecordsCode0
Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs—0
Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection—0
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified