Grewe, Florian
[VerfasserIn];
Owotoki, Peter
[VerfasserIn]
;
Gesellschaft für Informatik Fachbereich Künstliche Intelligenz Fachgruppe Knowledge Discovery, Data Mining und Maschinelles Lernen (KDML)
Automated Model Selection with AMSF in a production process of the automotive industry
Hochschulschrift:
Universität Hildesheim, Institut für Informatik, Tagungsbeitrag: 2006
Anmerkungen:
Beschreibung:
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