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Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams

机译:在线数据动态分类的分布式学习者组合

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We present a distributed online learning scheme to classify data captured from distributed and dynamic data sources. Our scheme consists of multiple distributed local learners, which analyze different streams of data that are correlated to a common event that needs to be classified. Each learner uses a local classifier to make a local prediction. The local predictions are then collected by each learner and combined using a weighted majority rule to output the final prediction. We propose a novel online ensemble learning algorithm to update the aggregation rule in order to adapt to the underlying data dynamics. We rigorously determine an upper bound for the worst-case mis-classification probability of our algorithm, which tends asymptotically to 0 if the mis-classification probability of the best (unknown) static aggregation rule is 0. Then we extend our algorithm to address challenges specific to the distributed implementation and prove new bounds that apply to these settings. Finally, we test our scheme by performing an evaluation study on several data sets.
机译:我们提出了一种分布式在线学习方案,以对从分布式和动态数据源捕获的数据进行分类。我们的方案由多个分布式本地学习者组成,他们分析与需要分类的常见事件相关的不同数据流。每个学习者都使用本地分类器进行本地预测。然后由每个学习者收集局部预测,并使用加权多数规则进行组合以输出最终预测。我们提出了一种新颖的在线集成学习算法来更新聚合规则,以适应基础的数据动态。我们严格确定算法最坏情况的误分类概率的上限,如果最佳(未知)静态聚合规则的误分类概率为0,则渐近趋势将趋近于0。然后我们扩展算法来应对挑战特定于分布式实现,并证明适用于这些设置的新界限。最后,我们通过对几个数据集进行评估研究来测试我们的方案。

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