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 1011–1020 of 1706 papers

TitleStatusHype
Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable EnergyCode0
A Robust Hybrid Approach for Textual Document ClassificationCode0
Deep Learning Regression of VLSI Plasma Etch Metrology—0
Multi-Task Bidirectional Transformer Representations for Irony Detection—0
AutoML for Contextual Bandits—0
One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar ClassificationCode0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMsCode0
Classifying single-qubit noise using machine learning—0
HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified