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Dynamically Selected Ensemble for Data Stream Classification

Mining data streams is a hot topic in the machine learning (ML) community. In addition to learning and updating accurate models over time, these techniques must respect constraints that are not necessarily as strong in batch mode, such as time processing and memory consumption efficiency. A successful family of techniques in batch ML is dynamic classifier selection (DCS). However, these are roughly overlooked in data stream mining. In this paper, we propose a novel dynamic classifier selection framework for data streams called Double Dynamic Classifier Selection (DDCS). We compare DDCS against state-of-art methods for mining data streams in both synthetic and realworld datasets. Results depict that DDCS not only outperforms the state-of-art ensemble methods for data stream classification in terms of accuracy but is also significantly more efficient in terms of processing time and memory consumption.