Batch Weighted Ensemble for Mining Data Streams with Concept Drift
نویسنده
چکیده
This paper presents a new framework for dealing with two main types of concept drift (sudden and gradual) in labeled data with decision attribute. The learning examples are processed instance by instance. This new framework, called Online Batch Weighted Ensemble, introduces element of incremental processing into a block-based ensemble of classi ers. Its performance was evaluated experimentally on data sets with di erent types of concept drift and compared with the performance of Accuracy Weighted Ensemble and Batch Weighted Ensemble. The results show that OBWE improves value of the total accuracy.
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تاریخ انتشار 2011