首页> 外国专利> GENERATING ACCURATE REASON CODES WITH COMPLEX NON-LINEAR MODELING AND NEURAL NETWORKS

GENERATING ACCURATE REASON CODES WITH COMPLEX NON-LINEAR MODELING AND NEURAL NETWORKS

机译:利用复杂的非线性建模和神经网络生成精确的原因码

摘要

A computer system computes a score for a received data exchange and, in accordance with a neural network and input variables determined by received current exchange and history data, the computed score indicates a condition suitable for a denial. A set of attribution scores are computed using an Alternating Decision Tree model in response to a computed score that is greater than a predetermined score threshold value for the denial. The computed score is provided to an assessment unit and, if the computed score indicates a condition suitable for the denial and if attribution scores are computed, then a predetermined number of input variable categories from a rank-ordered list of input variable categories is also provided to the assessment unit of the computer system.
机译:计算机系统计算接收到的数据交换的分数,并且根据神经网络和由接收到的当前交换和历史数据确定的输入变量,计算出的分数指示适于拒绝的条件。响应于所计算的分数大于用于拒绝的预定分数阈值,使用交替决策树模型来计算一组归属分数。将计算出的分数提供给评估单元,如果计算出的分数指示适合否决的条件,并且如果计算出归因分数,那么还将提供来自输入变量类别的排序列表的预定数量的输入变量类别到计算机系统的评估单元。

著录项

  • 公开/公告号WO2016070096A1

    专利类型

  • 公开/公告日2016-05-06

    原文格式PDF

  • 申请/专利权人 SAS INSTITUTE INC.;

    申请/专利号WO2015US58403

  • 发明设计人 DIEV VESSELIN;DUKE BRIAN LEE;

    申请日2015-10-30

  • 分类号G06N5/04;

  • 国家 WO

  • 入库时间 2022-08-21 14:17:59

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