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 1061–1070 of 1706 papers

TitleStatusHype
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget—0
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition—0
Shallow Discourse Parsing Using Convolutional Neural Network—0
Shallow Updates for Deep Reinforcement Learning—0
Shape-based Feature Engineering for Solar Flare Prediction—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
SHEF-MIME: Word-level Quality Estimation Using Imitation Learning—0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified