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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 1611–1620 of 1706 papers

TitleStatusHype
Learning Summary Prior Representation for Extractive Summarization—0
Transition-based Dependency DAG Parsing Using Dynamic Oracles—0
A Dual-Layer Semantic Role Labeling System—0
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory—0
KeLP: a Kernel-based Learning Platform for Natural Language Processing—0
Multi-level Translation Quality Prediction with QuEst++—0
Feature Selection for Short Text Classification using Wavelet Packet Transform—0
Feature Optimization for Constituent Parsing via Neural Networks—0
A Joint Model for Chinese Microblog Sentiment Analysis—0
Incremental Recurrent Neural Network Dependency Parser with Search-based Discriminative Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified