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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 1301–1325 of 1706 papers

TitleStatusHype
Neurology-as-a-Service for the Developing World—0
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text ScoringCode0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning—0
Deep Learning in Lexical Analysis and Parsing—0
Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure—0
Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory—0
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble—0
End-to-End Optimized Speech Coding with Deep Neural Networks—0
Deep Health Care Text Classification—0
Solving the "false positives" problem in fraud predictionCode0
Clickbait Detection in Tweets Using Self-attentive NetworkCode0
End-to-end Network for Twitter Geolocation Prediction and HashingCode0
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip—0
Identifying Quantum Phase Transitions with Adversarial Neural NetworksCode0
Clickbait detection using word embeddings—0
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks—0
A Deep Neural Network Approach To Parallel Sentence Extraction—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified