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 1491–1500 of 1706 papers

TitleStatusHype
ProjE: Embedding Projection for Knowledge Graph CompletionCode0
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging—0
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction—0
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification—0
Modeling Skip-Grams for Event Detection with Convolutional Neural Networks—0
Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data—0
Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings—0
Discourse Parsing with Attention-based Hierarchical Neural Networks—0
Neural Sentiment Classification with User and Product AttentionCode0
Learning Connective-based Word Representations for Implicit Discourse Relation Identification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified