A fully distributed framework for cost-sensitive data mining

Abstract

A fully distributed system was proposed for cost-effective data mining. The approach achieved higher accuracy than the centralized and partially distributed learning methods. It also incured less training time, without any communication or computational overheads. Experimental results showed that the total benefits were larger for the fully distributed system than for a partially distributed system using meta-learning.