Improving the performance of bagging ensembles for data streams through mini-batching
Guilherme Cassales,
Heitor Gomes,
Albert Bifet
et al.
Abstract:Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and transient behavior. Compared to traditional (batch) data mining, stream processing algorithms have additional requirements regarding computational resources and adaptability to data evolution. They must process instances incrementally because the data's continuous flow prohibits storing data for multiple passes. Ensemble learning achiev… Show more
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