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 1381–1390 of 1706 papers

TitleStatusHype
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity—0
A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task—0
A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings SurprisesCode0
Hyperbolic Representation Learning for Fast and Efficient Neural Question AnsweringCode0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Learning to Rank Question Answer Pairs with Holographic Dual LSTM ArchitectureCode0
Generalized Convolutional Neural Networks for Point Cloud Data—0
Automation of Feature Engineering for IoT Analytics—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified