Browse SoTA > Methodology > AutoML > Automated Feature Engineering

Automated Feature Engineering

10 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.

Benchmarks

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

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

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

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

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

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

Cardea: An Open Automated Machine Learning Framework for Electronic Health Records

1 Oct 2020DAI-Lab/Cardea

An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018.

AUTOMATED FEATURE ENGINEERING FEATURE ENGINEERING MODEL SELECTION

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

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

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

3 Sep 2019Yvan_Lucas/hmm-ccfd

Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.

AUTOMATED FEATURE ENGINEERING FEATURE ENGINEERING FRAUD DETECTION

Solving the "false positives" problem in fraud prediction

20 Oct 2017An0wn/machinelearning

In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.

AUTOMATED FEATURE ENGINEERING FEATURE ENGINEERING