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 1151–1175 of 1706 papers

TitleStatusHype
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data—0
Machine learning for predicting thermal power consumption of the Mars Express SpacecraftCode0
Neural Ranking Models for Temporal Dependency Structure ParsingCode0
Weakly-Supervised Neural Text ClassificationCode0
Revisiting Character-Based Neural Machine Translation with Capacity and Compression—0
Attention-based Neural Text SegmentationCode0
Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature AugmentationCode0
Disfluency Detection using Auto-Correlational Neural NetworksCode0
A strong baseline for question relevancy ranking—0
Learning behavioral context recognition with multi-stream temporal convolutional networks—0
Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or FemaleCode0
Multi-Level Network Embedding with Boosted Low-Rank Matrix ApproximationCode0
MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction—0
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction—0
Learning to Focus when Ranking Answers—0
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection—0
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models—0
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty DetectionCode0
deepQuest: A Framework for Neural-based Quality EstimationCode0
Seq2seq Dependency ParsingCode0
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling—0
A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages—0
Stance Detection with Hierarchical Attention Network—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
Active DOP: A constituency treebank annotation tool with online learningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified