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ENTROPY-BASED WEIGHTING IN RANDOM FOREST MODELS

机译:随机森林模型中基于熵的加权

摘要

A weighting value is determined for each of a plurality of decision trees in a random forest model hosted on a particular device, where the weighting is based on entropy of the respective decision tree. A new decision tree is received at the particular device and a weighting value is determined for the new decision tree based on entropy of the new decision tree. Based on the determined weighting value, it is determined whether to add the new the decision tree to the random forest model. A classification for data generated at the particular device is predicted using the random forest model.
机译:在特定设备上托管的随机森林模型中,为多个决策树中的每个决策树确定权重值,其中,权重基于相应决策树的熵。在特定设备处接收新的决策树,并基于新的决策树的熵为新的决策树确定权重值。基于确定的加权值,确定是否将新的决策树添加到随机森林模型。使用随机森林模型预测在特定设备上生成的数据的分类。

著录项

  • 公开/公告号US2018189667A1

    专利类型

  • 公开/公告日2018-07-05

    原文格式PDF

  • 申请/专利权人 INTEL CORPORATION;

    申请/专利号US201615393825

  • 发明设计人 YU-LIN TSOU;SHAO-WEN YANG;HONG-MIN CHU;

    申请日2016-12-29

  • 分类号G06N7;G06N99;G06N5/04;

  • 国家 US

  • 入库时间 2022-08-21 12:56:55

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