Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3156
Title: Automated machine learning in practice : state of the art and recent results
Authors : Tuggener, Lukas
Amirian, Mohammadreza
Rombach, Katharina
Lörwald, Stefan
Varlet, Anastasia
Westermann, Christian
Stadelmann, Thilo
et. al : No
Proceedings: 2019 6th Swiss Conference on Data Science (SDS)
Pages : 31
Pages to: 36
Conference details: 6th Swiss Conference on Data Science (SDS), Bern, 14 June 2019
Publisher / Ed. Institution : IEEE
Issue Date: 14-Jun-2019
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (publication)
Language : English
Subjects : AutoML; Meta learning; CASH; Portfolio hyperband; Learning to learn; Reinforcement learning
Subject (DDC) : 005: Computer programming, programs and data
Abstract: A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically—AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results of the most important AutoML algorithms.
Further description : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference paper
DOI : 10.21256/zhaw-3156
ISBN: 978-1-7281-3105-4
URI: https://digitalcollection.zhaw.ch/handle/11475/17502
Published as part of the ZHAW project : Ada – Advanced Algorithms for an Artificial Data Analyst
Appears in Collections:Publikationen School of Engineering

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