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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 1101–1125 of 1706 papers

TitleStatusHype
Supervised Typing of Big Graphs using Semantic Embeddings—0
Survey on Embedding Models for Knowledge Graph and its Applications—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection—0
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks—0
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields—0
Syntax Aware LSTM Model for Chinese Semantic Role Labeling—0
Syntax Aware LSTM model for Semantic Role Labeling—0
Syntax Encoding with Application in Authorship Attribution—0
Systematic Literature Review on Application of Machine Learning in Continuous Integration—0
SYSTRAN Participation to the WMT2018 Shared Task on Parallel Corpus Filtering—0
SZTE-NLP: Sentiment Detection on Twitter Messages—0
Tabular Feature Discovery With Reasoning Type Exploration—0
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration—0
Tackling Data Drift with Adversarial Validation: An Application for German Text Complexity Estimation—0
Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning—0
Tackling Sequence to Sequence Mapping Problems with Neural Networks—0
TaDeR: A New Task Dependency Recommendation for Project Management Platform—0
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news—0
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks—0
Team_BUDDI at ComMA@ICON: Exploring Individual and Joint Modelling Approaches for Detecting Aggression, Communal Bias and Gender Bias—0
Techniques for Automated Machine Learning—0
TEET! Tunisian Dataset for Toxic Speech Detection—0
Temperature Distribution Prediction in Laser Powder Bed Fusion using Transferable and Scalable Graph Neural Networks—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified