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 1281–1290 of 1706 papers

TitleStatusHype
A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Management—0
A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners—0
A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net—0
A Deep Learning Based Cost Model for Automatic Code Optimization—0
A deep learning model for estimating story points—0
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams—0
A Deep Neural Network Approach To Parallel Sentence Extraction—0
A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified