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Processing big data with decision trees: A case study in large traffic data

机译:使用决策树处理大数据:以大流量数据为例的案例研究

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This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.
机译:本文提供了使用决策树处理大型交通数据的比较。该实验在三种非常流行且在社区中广泛使用的分类器工具中进行了测试。这些分类器工具是WEKA分类器,MoA(大规模在线分析)分类器和在Hadoop基础架构上运行的SPARK MLib。我们使用决策树测试了流量数据,因为它是回归大数据的最佳方法之一。实验结果表明,当MoA成功将数据集作为数据流进行回归时,WEKA分类器无法对具有大量实例的数据集进行分类。 SPARK MLib决策树算法还可以以相当好的准确性快速成功地退回交通数据。

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