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    • 7. 发明授权
    • System and process for a fusion classification for insurance underwriting suitable for use by an automated system
    • 用于融合分类的系统和过程,适用于自动化系统使用的保险承保
    • US07383239B2
    • 2008-06-03
    • US10425721
    • 2003-04-30
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • G06F17/00G06N5/02
    • G06Q40/08G06Q40/00
    • A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.
    • 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说,分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度,然后可以使用比较模块来识别审计案例,增加用于重新调整生产的培训/测试集的案例 决策引擎,审查案例,或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示,这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望,例如,可以基于模型的平均过去精度,而使用动态贴现来表示分类器的可靠性的条件评估,例如,每当 分类器的输出基于不可靠的点数不足。
    • 8. 发明授权
    • System and process for a fusion classification for insurance underwriting suitable for use by an automated system
    • 用于融合分类的系统和过程,适用于自动化系统使用的保险承保
    • US08214314B2
    • 2012-07-03
    • US12131545
    • 2008-06-02
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • G06F17/00G06N5/02
    • G06Q40/08G06Q40/00
    • A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.
    • 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说,分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度,然后可以使用比较模块来识别审计案例,增加用于重新调整生产的培训/测试集的案例 决策引擎,审查案例,或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示,这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望,例如,可以基于模型的平均过去精度,而使用动态贴现来表示分类器的可靠性的条件评估,例如,每当 分类器的输出基于不可靠的点数不足。