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 1131–1140 of 1706 papers

TitleStatusHype
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment AnalysisCode0
IIT(BHU)--IIITH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological ReinflectionCode0
Cross-lingual Knowledge Graph Alignment via Graph Convolutional NetworksCode0
Syntax Encoding with Application in Authorship Attribution—0
Deep Exhaustive Model for Nested Named Entity Recognition—0
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection—0
Deep Attentive Sentence Ordering Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified