Scaling tree-based automated machine learning to biomedical big data with a feature set selector

Volume: 36, Issue: 1, Pages: 250 - 256
Published: Jun 4, 2019
Abstract
Motivation Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist’s prediction problem. However, like other AutoML...
Paper Details
Title
Scaling tree-based automated machine learning to biomedical big data with a feature set selector
Published Date
Jun 4, 2019
Volume
36
Issue
1
Pages
250 - 256
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