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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 801–810 of 1706 papers

TitleStatusHype
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs—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
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis—0
Breast mass classification in ultrasound based on Kendall's shape manifold—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
An Unsupervised Model with Attention Autoencoders for Question Retrieval—0
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified