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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 691–700 of 1706 papers

TitleStatusHype
Event Extraction with Generative Adversarial Imitation Learning—0
Event Nugget Detection with Forward-Backward Recurrent Neural Networks—0
Everybody likes short sentences - A Data Analysis for the Text Complexity DE Challenge 2022—0
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering—0
Building automated vandalism detection tools for Wikidata—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks—0
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface—0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified