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Automated Feature Engineering

7 papers with code · Methodology
Subtask of AutoML

Automated feature engineering improves upon the traditional approach to feature engineering by automatically extracting useful and meaningful features from a set of related data tables with a framework that can be applied to any problem.

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Latest papers with code

The autofeat Python Library for Automated Feature Engineering and Selection

22 Jan 2019cod3licious/autofeat

This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities.

AUTOMATED FEATURE ENGINEERING FEATURE ENGINEERING

108
22 Jan 2019

Benchmarking Automatic Machine Learning Frameworks

17 Aug 2018EpistasisLab/tpot

AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION

6,923
17 Aug 2018

Layered TPOT: Speeding up Tree-based Pipeline Optimization

18 Jan 2018EpistasisLab/tpot

With the demand for machine learning increasing, so does the demand for tools which make it easier to use.

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION

6,923
18 Jan 2018

AutoLearn - Automated Feature Generation and Selection

IEEE IEEE International Conference on Data Mining (ICDM) 2017 saket-maheshwary/AutoLearn

In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications.

AUTOMATED FEATURE ENGINEERING FEATURE ENGINEERING FEATURE IMPORTANCE

11
17 Nov 2017

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

20 Mar 2016rhiever/tpot

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION NEURAL ARCHITECTURE SEARCH

6,919
20 Mar 2016

ExploreKit: Automatic Feature Generation and Selection

ICDM 2016 2016 giladkatz/ExploreKit

To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features.

AUTOMATED FEATURE ENGINEERING FEATURE SELECTION

49
01 Jan 2016

Deep Feature Synthesis: Towards Automating Data Science Endeavors

DSAA 2015 2015 Featuretools/featuretools-docker

In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.

AUTOMATED FEATURE ENGINEERING

4
01 Jan 2015