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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 1526–1550 of 1706 papers

TitleStatusHype
Transition-Based Neural Word SegmentationCode0
A New Psychometric-inspired Evaluation Metric for Chinese Word Segmentation—0
Recognizing Salient Entities in Shopping Queries—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
A Sentence Interaction Network for Modeling Dependence between Sentences—0
Graph-based Dependency Parsing with Bidirectional LSTM—0
A Language-Independent Neural Network for Event Detection—0
Chinese Zero Pronoun Resolution with Deep Neural Networks—0
Improved Semantic Parsers For If-Then Statements—0
DeepSoft: A vision for a deep model of software—0
Deepr: A Convolutional Net for Medical Records—0
Modelling Context with User Embeddings for Sarcasm Detection in Social MediaCode1
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
Relation extraction from clinical texts using domain invariant convolutional neural network—0
Recurrent neural network models for disease name recognition using domain invariant features—0
Learning Concept Taxonomies from Multi-modal Data—0
Wide & Deep Learning for Recommender SystemsCode1
Incremental Parsing with Minimal Features Using Bi-Directional LSTM—0
Model-Agnostic Interpretability of Machine Learning—0
Neural Word Segmentation Learning for ChineseCode0
De-identification of Patient Notes with Recurrent Neural NetworksCode0
Learning Stylometric Representations for Authorship Analysis—0
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning—0
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Token-Level Metaphor Detection using Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified