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

2016 International Workshop on Big Data and Information Security (IWBIS) Pub Date : 2016-10-01 DOI:10.1109/IWBIS.2016.7872899

H. Wisesa, M. A. Ma'sum, P. Mursanto, A. Febrian

引用次数: 3

Abstract

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.

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用决策树处理大数据:大型交通数据的案例研究

本文对决策树在处理大型交通数据中的应用进行了比较。实验在三种不同的分类器工具中进行了测试，这些工具在社区中非常流行和广泛使用。这些分类器工具是WEKA分类器、MoA (Massive Online Analysis)分类器和运行在Hadoop基础设施上的SPARK MLib。我们使用决策树来测试交通数据，因为它是回归大数据的最佳方法之一。实验结果表明，WEKA分类器对具有大量实例的数据集无法进行分类，而MoA分类器则成功地将数据集回归为数据流。SPARK MLib决策树算法也可以成功地快速分解交通数据，并具有较好的精度。

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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2016 International Workshop on Big Data and Information Security (IWBIS)

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