Volltext-Downloads (blau) und Frontdoor-Views (grau)

Automated Model Selection with AMSFin a production process of the automotive industry

  • Machine learning, statistics and knowledge engineering provide a broad variety of supervised learning algorithms for classification. In this paper we introduce the Automated Model Selection Framework (AMSF) which presents automatic and semi-automatic methods to select classifiers. To achieve this we split up the selection process into three distinct phases. Two of those select algorithms by static rules which are derived from a manually created knowledgebase. At this stage of AMSF the user can choose between different rankers in the third phase. Currently, we use instance based learning and a scoring scheme for ranking the classifiers. After evaluation of different rankers we will recommend the most successful to the user by default. Besides describing the architecture and design issues, we additionally point out the versatile ways AMSF is applied in a production process of the automotive industry

Download full text files

Export metadata

Additional Services

Share in Twitter    Search Google Scholar    frontdoor_oas
Metadaten
Author:Florian Grewe, Peter Owotoki
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-299
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2011/04/18
Contributing Corporation:Hamburg University of Technology (TUHH)
Release Date:2011/04/18
Source:LWA 2006: Lernen - Wissensentdeckung - Adaptivität, Hildesheim, 9. - 11. Oktober 2006
PPN:Link zum Katalog
Contributor:Althoff, Klaus-Dieter
Institutes:Fachbereich IV / Informatik
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Licence (German):License LogoDeutsches Urheberrecht