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    Systems and methods for multi-objective portfolio analysis using pareto sorting evolutionary algorithms
    3.
    发明授权
    Systems and methods for multi-objective portfolio analysis using pareto sorting evolutionary algorithms 有权
    使用帕累托分类进化算法进行多目标投资组合分析的系统和方法

    公开(公告)号：US08219477B2

    公开(公告)日：2012-07-10

    申请号：US10781805

    申请日：2004-02-20

    申请人： Rajesh Venkat Subbu ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer

    发明人： Rajesh Venkat Subbu ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer

    IPC分类号： G06Q40/00

    CPC分类号： G06Q40/06

    摘要： The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations; committing the initial population of solutions to an initial population archive; performing a multi-objective process, based on the initial population archive and on multiple competing objectives, to generate an efficient frontier, the multi-objective process including a evolutionary algorithm process, the evolutionary algorithm process utilizing a dominance filter, the efficient frontier being used in investment decisioning.

    摘要翻译： 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：生成投资组合分配的最初解决方案群体; 将最初的人口解决方案提交给初始人口档案; 基于初始人口档案和多个竞争目标，进行多目标过程，以产生有效的前沿，多目标过程包括进化算法过程，利用优势过滤器的进化算法过程，使用的有效边界 投资决策。

    System and process for dominance classification for insurance underwriting suitable for use by an automated system
    4.
    发明授权
    System and process for dominance classification for insurance underwriting suitable for use by an automated system 有权
    适用于自动化系统的保险承保优势分类系统和流程

    公开(公告)号：US07567914B2

    公开(公告)日：2009-07-28

    申请号：US10425723

    申请日：2003-04-30

    申请人： Piero Patrone Bonissone ,  Naresh Sundaram Iyer

    发明人： Piero Patrone Bonissone ,  Naresh Sundaram Iyer

    IPC分类号： G06Q40/00

    CPC分类号： G06Q10/10 ,  G06F19/00 ,  G06Q40/02 ,  G06Q40/08 ,  G06Q50/22 ,  G06Q50/24

    摘要： A risk classification technique that exploits the existing risk structure of the decision problem in order to produce risk categorizations for new candidates is described. The technique makes use of a set of candidates for which risk categories have already been assigned (in the case of insurance underwriting, for example, this would pertain to the premium class assigned to an application). Using this set of labeled candidates, the technique produces two subsets for each risk category: the Pareto-best subset and the Pareto-worst subset by using Dominance. These two subsets can be seen as representing the least risky and the most risky candidates within a given risk category. If there are a sufficient number of candidates in these two subsets, then the candidates in these two subsets can be seen as samples from the two hypothetical risk surfaces in the feature space that bound the risk category from above and below respectively. A new candidate is assigned a risk category by verifying if the candidate lies within these two bounding risk surfaces.

    摘要翻译： 描述了利用现有风险决策问题的风险分类技术，以便为新候选人提供风险分类。 该技术利用已经分配了风险类别的一组候选人（在保险承保的情况下，例如，这将涉及分配给应用程序的溢价级别）。 使用这组标记候选者，该技术为每个风险类别产生两个子集：通过使用Dominance，帕累托最佳子集和帕累托最差子集。 这两个子集可以被视为代表风险类别中风险最低，风险最高的候选人。 如果这两个子集中有足够数量的候选人，那么这两个子集中的候选人可以被看作是从特征空间的两个假设风险面的样本，分别从上下分别界定风险类别。 通过验证候选人是否位于这两个边界风险表面内，为新的候选人分配了风险类别。

    System and process for a fusion classification for insurance underwriting suitable for use by an automated system
    5.
    发明授权
    System and process for a fusion classification for insurance underwriting suitable for use by an automated system 有权
    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

    公开(公告)号：US07383239B2

    公开(公告)日：2008-06-03

    申请号：US10425721

    申请日：2003-04-30

    申请人： Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

    发明人： Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

    IPC分类号： G06F17/00 ,  G06N5/02

    CPC分类号： G06Q40/08 ,  G06Q40/00

    摘要： 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.

    摘要翻译： 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。

    Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis
    7.
    发明授权
    Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis 失效
    多目标投资组合分析中有效前沿补充的系统和方法

    公开(公告)号：US07469228B2

    公开(公告)日：2008-12-23

    申请号：US10781897

    申请日：2004-02-20

    申请人： Piero Patrone Bonissone ,  Srinivas Bollapragada ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    发明人： Piero Patrone Bonissone ,  Srinivas Bollapragada ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    IPC分类号： G06Q40/00

    CPC分类号： G06Q40/06

    摘要： The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: performing a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; observing the generated efficient frontier; based on the observing, identifying an area of the efficient frontier in which there is a gap; and effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the efficient frontier being used in investment decisioning.

    摘要翻译： 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成投资组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：基于竞争目标执行第一多目标优化过程 ，以产生可能的解决方案的有效前沿; 观察生成的有效边界; 根据观察，确定存在差距的有效边界的一个区域; 并在差距填补过程中，有效的前沿在差距领域得到补充，有效的前沿被用于投资决策。

    Systems and methods for initial sampling in multi-objective portfolio analysis
    8.
    发明授权
    Systems and methods for initial sampling in multi-objective portfolio analysis 有权
    多目标投资组合分析初始抽样的系统和方法

    公开(公告)号：US08126795B2

    公开(公告)日：2012-02-28

    申请号：US10781898

    申请日：2004-02-20

    申请人： Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    发明人： Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    IPC分类号： G06Q40/00

    CPC分类号： G06Q40/06

    摘要： The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations, the generating the initial population of solutions of portfolio allocations including systematically generating the initial population of solutions to substantially cover the space defined by the competing objectives and the plurality of constraints; and generating an efficient frontier in the space based on the initial population, the efficient frontier for use in investment decisioning.

    摘要翻译： 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：生成投资组合分配的初始解决方案群体， 包括投资组合分配的最初解决方案，其中包括系统地产生解决方案的初始群体，以充分覆盖竞争目标和多个约束所界定的空间; 并根据初始人口，投资决策中使用的有效前沿，在空间中产生有效的前沿。

    System and method for defining normal operating regions and identifying anomalous behavior of units within a fleet, operating in a complex, dynamic environment
    9.
    发明授权
    System and method for defining normal operating regions and identifying anomalous behavior of units within a fleet, operating in a complex, dynamic environment 有权
    用于定义正常运行区域的系统和方法，并识别在复杂的动态环境中运行的机队内的单元的异常行为

    公开(公告)号：US07937334B2

    公开(公告)日：2011-05-03

    申请号：US11755924

    申请日：2007-05-31

    申请人： Piero Patrone Bonissone ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Kai Goebel ,  Anil Varma

    发明人： Piero Patrone Bonissone ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Kai Goebel ,  Anil Varma

    IPC分类号： G06F17/00

    CPC分类号： G05B23/024 ,  G06K9/6284 ,  G06N99/005

    摘要： Monitoring dynamic units that operate in complex, dynamic environments, is provided in order to classify and track unit behavior over time. When domain knowledge is available, feature-based models may be used to capture the essential state information of the units. When domain knowledge is not available, raw data is relied upon to perform this task. By analyzing logs of event messages (without having access to their data dictionary), embodiments allow the identification of anomalies (novelties). Specifically, a Normalized Compression Distance (such as one based on Kolmogorov Complexity) may be applied to logs of event messages. By analyzing the similarity and differences of the event message logs, units are identified that did not experience any abnormality (and locate regions of normal operations) and units that departed from such regions.

    摘要翻译： 提供了监控在复杂，动态环境中运行的动态单元，以便对时间段内的单元行为进行分类和跟踪。 当领域知识可用时，可以使用基于特征的模型来捕获单位的基本状态信息。 当领域知识不可用时，依靠原始数据来执行此任务。 通过分析事件消息的日志（不访问其数据字典），实施例允许识别异常（新奇事物）。 具体来说，归一化压缩距离（例如基于Kolmogorov复杂度的距离）可以应用于事件消息的日志。 通过分析事件消息日志的相似性和差异，识别出没有经历任何异常（并定位正常操作的区域）的单位和离开这些区域的单位。