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 931–940 of 1706 papers

TitleStatusHype
Embedding WordNet Knowledge for Textual Entailment—0
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM—0
EmotionX-SmartDubai\_NLP: Detecting User Emotions In Social Media Text—0
EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes—0
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell—0
Empty Category Detection With Joint Context-Label Embeddings—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
End-to-End Argument Mining as Biaffine Dependency Parsing—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified