Showing 1 - 10 of 20
This paper analyses the complexity of rule selection for supervised learning in distributed scenarios. The selection of rules is usually guided by a utility measure such as predictive accuracy or weighted relative accuracy. Other examples are support and con dence, known from association rule...
Boosting algorithms for classification are based on altering the ini- tial distribution assumed to underly a given example set. The idea of knowledge-based sampling (KBS) is to sample out prior knowledge and previously discovered patterns to achieve that subsequently ap- plied data mining...
Die Grundlage für das neue Aufsichtssystem, das im Rahmen des Solvency II-Projektes in derVersicherungswirtschaft eingeführt wird, bildet das Risikoverständnis seitens der Unternehmensführung.1 Bei der Diskussion unternehmerischer Entscheidungsfragen müssen die Risikosituation desBetriebes...
This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It...