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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 451–475 of 1706 papers

TitleStatusHype
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
Cross-lingual Short-text Matching with Deep Learning—0
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification—0
Democratizing AI: Non-expert design of prediction tasks—0
autoNLP: NLP Feature Recommendations for Text Analytics Applications—0
Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network—0
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models—0
Customers Churn Prediction in Financial Institution Using Artificial Neural Network—0
A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities—0
A strong baseline for question relevancy ranking—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
A Comparative Analysis of Android Malware—0
Data-driven intelligent computational design for products: Method, techniques, and applications—0
Data-Driven Investigative Journalism For Connectas Dataset—0
Data-driven Smart Ponzi Scheme Detection—0
Dataiku's Solution to SPHERE's Activity Recognition Challenge—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems—0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends—0
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison—0
A Survey on Churn Analysis—0
Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data—0
Deep Learning for Iris Recognition: A Review—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified