Scalable stream Bayes classification based on Dirichlet prior

O. Bina, Yuan Yanhua
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Abstract

Learning from fast data stream is one of the most challenging tasks in data stream mining. The fact that data streams are unbounded sequences, highlights exclusive challenges in contrast to classifiers from batch data. Most of methods aren't naturally parallel and thus their scalability is limited. This paper proposes a scalable data stream Bayes classifier utilizing a new estimation(DIB). The new estimation takes conjugate Dirichlet prior as parameter's prior distribution and thus improves the predictive accuracy. Meanwhile, this paper proposes a new distributed implementation of DIB on Flink. Experiments show that DIB classifier significantly outperforms Naïve Bayes in terms of accuracy. Also, the experiment proves parallel DIB running on Flink enhances the throughput and reduces execution time.
基于Dirichlet先验的可扩展流贝叶斯分类
从快速数据流中学习是数据流挖掘中最具挑战性的任务之一。数据流是无界序列的事实,与来自批处理数据的分类器相比,突出了排他性挑战。大多数方法不是自然并行的,因此它们的可伸缩性是有限的。本文提出了一种基于新估计(DIB)的可扩展数据流贝叶斯分类器。新估计采用共轭狄利克雷先验作为参数的先验分布,提高了预测精度。同时,本文提出了一种新的基于Flink的分布式DIB实现方法。实验表明,DIB分类器在准确率上明显优于Naïve贝叶斯。实验还证明,在Flink上运行并行DIB可以提高吞吐量,减少执行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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