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...

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...

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 confidence, known from association rule...