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    Methods for multi-class cost-sensitive learning
    2.
    发明授权
    Methods for multi-class cost-sensitive learning 有权
    多类成本敏感性学习方法

    公开(公告)号：US07558764B2

    公开(公告)日：2009-07-07

    申请号：US11937629

    申请日：2007-11-09

    申请人： Naoki Abe ,  Bianca Zadrozny

    发明人： Naoki Abe ,  Bianca Zadrozny

    IPC分类号： G06F15/18 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00

    CPC分类号： G06N99/005 ,  G06K9/6256 ,  Y10S706/932

    摘要： Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively. It then finally outputs a classifier hypothesis which is the average of all the hypotheses output in the respective iterations.

    摘要翻译： 多类成本敏感学习的方法基于迭代示例加权方案，并使用二进制分类算法解决多类成本敏感学习问题。 其中一种方法通过迭代地应用来自扩展数据集的加权采样来工作，该扩展数据集通过使用赋予每个实例的加权方案来增强具有尽可能多的数据点的数据点与原始数据集中的每个示例而获得的加权采样 标示的重量指定为该实例的平均成本与目前为止的迭代的平均假设之间的差异以及与所标记的示例中的标签相关联的错误分类成本。 然后，对修改后的二进制分类问题调用组件分类算法，其中每个示例本身已经是一个标记对，根据上述加权方案中的示例权重是正还是负，其（元）标签为1或0， 分别。 然后，它最终输出一个分类器假设，它是相应迭代中输出的所有假设的平均值。

    Ranking-based method for evaluating customer prediction models
    4.
    发明授权
    Ranking-based method for evaluating customer prediction models 有权
    基于排名的评估客户预测模型的方法

    公开(公告)号：US07725340B2

    公开(公告)日：2010-05-25

    申请号：US12050371

    申请日：2008-03-18

    申请人： Claudia Reisz ,  Saharon Rosset ,  Bianca Zadrozny

    发明人： Claudia Reisz ,  Saharon Rosset ,  Bianca Zadrozny

    IPC分类号： G06F17/18

    CPC分类号： G06Q30/02 ,  G06Q10/063 ,  G06Q10/063112 ,  G06Q10/0639 ,  G06Q30/0202 ,  G06Q30/0204

    摘要： A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

    摘要翻译： 一种方法和系统对于经常适用于营销任务的回归模型执行基于排序的评估，并且比传统的基于剩余的绩效指标更为鲁棒。 方法和系统提供的输出提供可视化，可以提供有关本地模型性能和异常值的见解。 可以将几个模型相互比较，以确定“最佳”模型，从而确定特定营销任务的“最佳”模型数据。

    RANKING-BASED METHOD AND SYSTEM FOR EVALUATING CUSTOMER PREDICATION MODELS
    5.
    发明申请
    RANKING-BASED METHOD AND SYSTEM FOR EVALUATING CUSTOMER PREDICATION MODELS 审中-公开
    基于排序的方法和系统评估客户预测模型

    公开(公告)号：US20080015910A1

    公开(公告)日：2008-01-17

    申请号：US11456663

    申请日：2006-07-11

    申请人： Claudia Reisz ,  Saharon Rosset ,  Bianca Zadrozny

    发明人： Claudia Reisz ,  Saharon Rosset ,  Bianca Zadrozny

    IPC分类号： G06F17/50

    CPC分类号： G06Q30/02 ,  G06Q10/063 ,  G06Q10/063112 ,  G06Q10/0639 ,  G06Q30/0202 ,  G06Q30/0204

    摘要： A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

    摘要翻译： 一种方法和系统对于经常适用于营销任务的回归模型执行基于排序的评估，并且比传统的基于剩余的绩效指标更为鲁棒。 方法和系统提供的输出提供可视化，可以提供有关本地模型性能和异常值的见解。 可以将几个模型相互比较，以确定“最佳”模型，从而确定特定营销任务的“最佳”模型数据。

    METHODS FOR MULTI-CLASS COST-SENSITIVE LEARNING
    6.
    发明申请
    METHODS FOR MULTI-CLASS COST-SENSITIVE LEARNING 有权
    多级成本敏感性学习方法

    公开(公告)号：US20080065572A1

    公开(公告)日：2008-03-13

    申请号：US11937629

    申请日：2007-11-09

    申请人： Naoki Abe ,  Bianca Zadrozny

    发明人： Naoki Abe ,  Bianca Zadrozny

    IPC分类号： G06N3/00

    CPC分类号： G06N99/005 ,  G06K9/6256 ,  Y10S706/932

    摘要： Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively. It then finally outputs a classifier hypothesis which is the average of all the hypotheses output in the respective iterations.

    摘要翻译： 多类成本敏感学习的方法基于迭代示例加权方案，并使用二进制分类算法解决多类成本敏感学习问题。 其中一种方法通过迭代地应用来自扩展数据集的加权采样来工作，该扩展数据集通过使用给出每个实例的加权方案来增强原始数据集中具有尽可能多的数据点的数据点与任何单个实例的可能标签而获得的每个示例而获得 标示的重量指定为该实例的平均成本与目前为止的迭代的平均假设之间的差异以及与所标记的示例中的标签相关联的错误分类成本。 然后，对修改后的二进制分类问题调用组件分类算法，其中每个示例本身已经是一个标记对，根据上述加权方案中的示例权重是正还是负，其（元）标签为1或0， 分别。 然后，它最终输出一个分类器假设，它是相应迭代中输出的所有假设的平均值。

    Methods for multi-class cost-sensitive learning
    7.
    发明申请
    Methods for multi-class cost-sensitive learning 审中-公开
    多类成本敏感性学习方法

    公开(公告)号：US20050289089A1

    公开(公告)日：2005-12-29

    申请号：US10876533

    申请日：2004-06-28

    申请人： Naoki Abe ,  Bianca Zadrozny

    发明人： Naoki Abe ,  Bianca Zadrozny

    IPC分类号： G06F9/44 ,  G06F15/18 ,  G06K9/62 ,  G06N3/08 ,  G06N7/02 ,  G06N7/06

    CPC分类号： G06K9/6256 ,  G06N20/00 ,  Y10S706/932

    摘要： Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively. It then finally outputs a classifier hypothesis which is the average of all the hypotheses output in the respective iterations.

    摘要翻译： 多类成本敏感学习的方法基于迭代示例加权方案，并使用二进制分类算法解决多类成本敏感学习问题。 其中一种方法通过迭代地应用来自扩展数据集的加权采样来工作，该扩展数据集通过使用给出每个实例的加权方案来增强原始数据集中具有尽可能多的数据点的数据点与任何单个实例的可能标签而获得的每个示例而获得 标示的重量指定为该实例的平均成本与目前为止的迭代的平均假设之间的差异以及与所标记的示例中的标签相关联的错误分类成本。 然后，对修改后的二进制分类问题调用组件分类算法，其中每个示例本身已经是一个标记对，根据上述加权方案中的示例权重是正还是负，其（元）标签为1或0， 分别。 然后，它最终输出一个分类器假设，它是相应迭代中输出的所有假设的平均值。