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 1051–1075 of 1706 papers

TitleStatusHype
Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
Fourier Transform Approach to Machine Learning III: Fourier Classification—0
F-RBA: A Federated Learning-based Framework for Risk-based Authentication—0
Free-Text Keystroke Dynamics for User Authentication—0
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations—0
From Digital Humanities to Quantum Humanities: Potentials and Applications—0
From Features to Transformers: Redefining Ranking for Scalable Impact—0
Gated Recursive and Sequential Deep Hierarchical Encoding for Detecting Incongruent News Articles—0
Gated Recursive Neural Network for Chinese Word Segmentation—0
GBD-NER at PARSEME Shared Task 2018: Multi-Word Expression Detection Using Bidirectional Long-Short-Term Memory Networks and Graph-Based Decoding—0
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model—0
Generalized Convolutional Neural Networks for Point Cloud Data—0
Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
GeoDecoder: Empowering Multimodal Map Understanding—0
Geomancer: An Open-Source Framework for Geospatial Feature Engineering—0
Geometric feature performance under downsampling for EEG classification tasks—0
Getting the Most out of AMR Parsing—0
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases—0
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition—0
Global Pose Estimation with an Attention-based Recurrent Network—0
Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified