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

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Showing 891–900 of 1706 papers

TitleStatusHype
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate—0
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks—0
Supervised learning on heterogeneous, attributed entities interacting over time—0
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates—0
Product age based demand forecast model for fashion retail—0
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network—0
CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space—0
A Simple and Effective Dependency Parser for Telugu—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified