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公开(公告)号：US20100287167A1
公开(公告)日：2010-11-11
申请号：US12430818
申请日：2009-04-27
申请人： Christopher Allan Ralph , Michael S. Sossi , Stefan Kuzminski , Helen Geraldine E. Rosario , Yaxin Liu
发明人： Christopher Allan Ralph , Michael S. Sossi , Stefan Kuzminski , Helen Geraldine E. Rosario , Yaxin Liu
IPC分类号： G06F17/30
CPC分类号： G06Q10/10
摘要： A system and method for building segmented scorecards for a population is presented. A model of the population is built using a model builder computer, and one or more variables used by the model builder to build the model is stored in a repository. A scorecard is generated for each segment of the population based on the model and using an adaptive random tree computer program. Next, the scorecard for each segment is enhanced using a integer non-linear programming computer program to determine optimal score weights associated with the variables used by the model builder to build the model, and to generate an enhanced segmented scorecard for the population.
摘要翻译： 提出了一种为人口建立分段记分卡的系统和方法。 使用模型构建器计算机构建人口模型，模型构建器用于构建模型的一个或多个变量存储在存储库中。 基于模型并使用自适应随机树计算机程序为每个群体生成记分卡。 接下来，使用整数非线性规划计算机程序来增强每个段的记分卡，以确定与由模型构建器构建模型所使用的变量相关联的最佳分数权重，并为群体生成增强的分段记分卡。
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公开(公告)号：US08255423B2
公开(公告)日：2012-08-28
申请号：US12430818
申请日：2009-04-27
申请人： Christopher Allan Ralph , Michael S. Sossi , Stefan Kuzminski , Helen Geraldine E. Rosario , Yaxin Liu
发明人： Christopher Allan Ralph , Michael S. Sossi , Stefan Kuzminski , Helen Geraldine E. Rosario , Yaxin Liu
IPC分类号： G06F7/00
CPC分类号： G06Q10/10
摘要： A system and method for building segmented scorecards for a population is presented. A model of the population is built using a model builder computer, and one or more variables used by the model builder to build the model is stored in a repository. A scorecard is generated for each segment of the population based on the model and using an adaptive random tree computer program. Next, the scorecard for each segment is enhanced using a integer non-linear programming computer program to determine optimal score weights associated with the variables used by the model builder to build the model, and to generate an enhanced segmented scorecard for the population.
摘要翻译： 提出了一种为人口建立分段记分卡的系统和方法。 使用模型构建器计算机构建人口模型，模型构建器用于构建模型的一个或多个变量存储在存储库中。 基于模型并使用自适应随机树计算机程序为每个群体生成记分卡。 接下来，使用整数非线性规划计算机程序来增强每个段的记分卡，以确定与由模型构建器构建模型所使用的变量相关联的最佳分数权重，并为群体生成增强的分段记分卡。
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公开(公告)号：US20100049665A1
公开(公告)日：2010-02-25
申请号：US12430806
申请日：2009-04-27
IPC分类号： G06Q40/00
摘要： A system and method for identifying homogeneous risk pools used in the calculation of minimum capital requirements for a number of segments of a population of portfolios is presented. An F-ratio objective function representing a probability of a risk event across all of the number of segments of the population is calculated using an F-ratio objective function engine. An input dataset that defines a decision tree structure for the population is received. The F-ratio objective function of the risk event is maximized using a generic algorithm-based search engine to optimize the decision tree structure to group the number of segments according to one or more of the homogeneous risk pools, and a score for each homogeneous risk pool is then generated.
摘要翻译： 提出了一种用于确定用于计算投资组合中多个部分的最低资本要求的均匀风险池的系统和方法。 使用F比率目标函数引擎来计算表示总体上所有数量段的风险事件的概率的F比率目标函数。 接收为群体定义决策树结构的输入数据集。 风险事件的F比率目标函数使用通用的基于算法的搜索引擎来最大化，以优化决策树结构，以根据一个或多个同质风险池对段数进行分组，以及每个均匀风险的得分 然后生成池。
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公开(公告)号：US07895139B2
公开(公告)日：2011-02-22
申请号：US11877626
申请日：2007-10-23
申请人： Gary J. Sullivan , Helen Geraldine E. Rosario , Michael S. Sossi , Christopher Ralph , John Duchnowski
发明人： Gary J. Sullivan , Helen Geraldine E. Rosario , Michael S. Sossi , Christopher Ralph , John Duchnowski
IPC分类号： G06N5/00
CPC分类号： G06N3/126
摘要： Data spiders, provide an automated system that can take a file or file store of historic transaction data and create the best set of variables from that data, where “best” means highly predictive. Genetic algorithms are used to parameterized transactions to form groups, which are subjected naïve Bayes score ranking. Variable groups are generated and ranked accord to the score. Data spiders span the full information available, are uncorrelated with previous methods, and are easily interpretable.
摘要翻译： 数据蜘蛛提供了一个自动化系统，可以获取历史交易数据的文件或文件存储，并从该数据中创建最佳的变量集，其中“最佳”意味着高度预测。 遗传算法用于参数化交易以形成组，其受到朴素的贝叶斯评分排名。 生成变量组并按照得分排序。 数据蜘蛛涵盖了可用的全部信息，与以前的方法不相关，并且易于解释。
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公开(公告)号：US20080177681A1
公开(公告)日：2008-07-24
申请号：US11877626
申请日：2007-10-23
IPC分类号： G06N3/12
CPC分类号： G06N3/126
摘要： Data spiders, provide an automated system that can take a file or file store of historic transaction data and create the best set of variables from that data, where “best” means highly predictive. Genetic algorithms are used to parameterized transactions to form groups, which are subjected naïve Bayes score ranking. Variable groups are generated and ranked accord to the score. Data spiders span the full information available, are uncorrelated with previous methods, and are easily interpretable.
摘要翻译： 数据蜘蛛提供了一个自动化系统，可以获取历史交易数据的文件或文件存储，并从该数据中创建最佳的变量集，其中“最佳”意味着高度预测。 遗传算法用于参数化交易以形成组，其受到朴素的贝叶斯评分排名。 生成变量组并按照得分排序。 数据蜘蛛涵盖了可用的全部信息，与以前的方法不相关，并且易于解释。
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