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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 1326–1350 of 1706 papers

TitleStatusHype
Clickbait detection using word embeddings—0
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks—0
Graph Convolutional Networks for Named Entity RecognitionCode0
A Deep Neural Network Approach To Parallel Sentence Extraction—0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges—0
Investigating how well contextual features are captured by bi-directional recurrent neural network models—0
Do LSTMs really work so well for PoS tagging? -- A replication study—0
Getting the Most out of AMR Parsing—0
Recurrent Attention Network on Memory for Aspect Sentiment AnalysisCode0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Syntax Aware LSTM model for Semantic Role Labeling—0
A Cognition Based Attention Model for Sentiment Analysis—0
Deep Neural Solver for Math Word Problems—0
A study of N-gram and Embedding Representations for Native Language IdentificationCode0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
Chinese Zero Pronoun Resolution with Deep Memory Network—0
Transparent text quality assessment with convolutional neural networks—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
A Factored Neural Network Model for Characterizing Online Discussions in Vector SpaceCode0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages—0
Feature-Enriched Character-Level Convolutions for Text Regression—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified