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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 801–825 of 1706 papers

TitleStatusHype
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
Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way—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
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model—0
Generalized Convolutional Neural Networks for Point Cloud Data—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation—0
An Efficient Architecture for Predicting the Case of Characters using Sequence Models—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction—0
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks—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
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition—0
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified