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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 1321–1330 of 1706 papers

TitleStatusHype
Graph Convolutional Networks for Named Entity RecognitionCode0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges—0
Investigating how well contextual features are captured by bi-directional recurrent neural network models—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages—0
Feature-Enriched Character-Level Convolutions for Text Regression—0
Transparent text quality assessment with convolutional neural networks—0
A study of N-gram and Embedding Representations for Native Language IdentificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified