Automated Feature Engineering

17 papers with code • 0 benchmarks • 0 datasets

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.

Most implemented papers

Benchmarking Automatic Machine Learning Frameworks

EpistasisLab/tpot 17 Aug 2018

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

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

rhiever/tpot 20 Mar 2016

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.

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

DAI-Lab/Cardea 1 Oct 2020

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

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

uts-caslab/autoweka 23 Dec 2020

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms.

Supervised Video Summarization via Multiple Feature Sets with Parallel Attention

TIBHannover/MSVA 23 Apr 2021

The proposed architecture utilizes an attention mechanism before fusing motion features and features representing the (static) visual content, i. e., derived from an image classification model.

Deep Feature Synthesis: Towards Automating Data Science Endeavors

Featuretools/featuretools-docker DSAA 2015 2015

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

ExploreKit: Automatic Feature Generation and Selection

giladkatz/ExploreKit ICDM 2016 2016

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.

Solving the "false positives" problem in fraud prediction

An0wn/machinelearning 20 Oct 2017

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

AutoLearn - Automated Feature Generation and Selection

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

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.

Layered TPOT: Speeding up Tree-based Pipeline Optimization

EpistasisLab/tpot 18 Jan 2018

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