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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1526–1550 of 1706 papers

TitleStatusHype
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
Shallow Discourse Parsing Using Convolutional Neural Network—0
Recognizing Salient Entities in Shopping Queries—0
Adapting Event Embedding for Implicit Discourse Relation Recognition—0
Word embeddings and discourse information for Quality Estimation—0
Transition-Based Neural Word SegmentationCode0
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
Improving Sequence to Sequence Learning for Morphological Inflection Generation: The BIU-MIT Systems for the SIGMORPHON 2016 Shared Task for Morphological Reinflection—0
DeepSoft: A vision for a deep model of software—0
Deepr: A Convolutional Net for Medical Records—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
Recurrent neural network models for disease name recognition using domain invariant features—0
Relation extraction from clinical texts using domain invariant convolutional neural network—0
Learning Concept Taxonomies from Multi-modal Data—0
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
Dynamic Feature Induction: The Last Gist to the State-of-the-Art—0
Show:102550
← PrevPage 62 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified