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Quantity forecast of administrative items based on parallel random forest

机译：基于并行随机森林的行政项目数量预测

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摘要

The ultimate goal of this paper is to train a model based on the given administrative data to predict the amount of each administrative item of month in different years and different regions as accurate as possible. In this paper, we propose a novel approach for quantity forecast of administrative data which is named after parallel random forest (parallel RF). Firstly, we collect administrative data from different online systems using java program and store it in MongoDB. Then we extract key information from these data and assign different numbers to different administrative areas and item names. Next, as the core of whole method, we train the prediction model by implementing the random forest method on Hadoop Map-Reduce. Finally, we compare the execution efficiency and prediction accuracy with other standard algorithms such as SVM and gradient boosting. The experiment shows that the accuracy and efficiency of our method is much better than other algorithms and our method is more reliable and useful.
机译：本文的最终目标是根据给定的管理数据训练模型，以尽可能准确地预测不同年份和不同地区中每个月份的管理项目数量。在本文中，我们提出了一种用于管理数据量预测的新方法，该方法以并行随机森林（parallel RF）命名。首先，我们使用Java程序从不同的联机系统收集管理数据，并将其存储在MongoDB中。然后，我们从这些数据中提取关键信息，并为不同的管理区域和项目名称分配不同的编号。接下来，作为整体方法的核心，我们通过在Hadoop Map-Reduce上实现随机森林方法来训练预测模型。最后，我们将执行效率和预测精度与其他标准算法（例如SVM和梯度提升）进行比较。实验表明，该方法的准确性和效率均优于其他算法，并且更加可靠，实用。

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  • 来源
  • 会议地点 Shanghai(CN)
  • 作者

    Linxia Zhong; Wanggen Wan; Ziyue Luo; Xiaodong Zhang;

  • 作者单位

    School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;

    School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;

    School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;

    School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    1/f noise;

    机译：1 / f噪音；;

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