公开(公告)号：US20100161526A1

公开(公告)日：2010-06-24

申请号：US12340168

申请日：2008-12-19

Applicant: Jianping ZHANG ,  Brent M. Han ,  Ali R. Hadjarian

Inventor： Jianping ZHANG ,  Brent M. Han ,  Ali R. Hadjarian

IPC: G06F15/18 ,  G06N5/02

CPC classification number: G06N5/025

Abstract: Systems, methods and computer program products for the ranking of a target data set based on learned rules are disclosed. One embodiment is a method that includes generating a learned rule set from a training data record set, creating at least one prototype for each rule in the learned rule set to generate a prototype set, and ranking the target data record set using learned rule set and the prototype set. The generating of a learned rule set includes dividing the training data record set to a positive class and a negative class, and deriving the learned rule set for the positive class. Learning of rules includes deriving the most general projected rules with respect to remaining training data and then refining those rules, eventually selecting the best rules using an F-measure.

Abstract translation: 公开了基于学习规则对目标数据集进行排序的系统，方法和计算机程序产品。 一个实施例是一种方法，其包括从训练数据记录集生成学习规则集，为学习规则集中的每个规则创建至少一个原型以生成原型集，并且使用学习规则集对目标数据记录集进行排序，以及 原型集。 学习规则集的生成包括将训练数据记录集划分为正类和负类，并且导出积极类的学习规则集。 规则的学习包括获得关于剩余训练数据的最一般的预测规则，然后改进这些规则，最终使用F度量来选择最佳规则。

公开(公告)号：US08341149B2

公开(公告)日：2012-12-25

申请号：US12340168

申请日：2008-12-19

Applicant: Jianping Zhang ,  Brent M. Han ,  Ali R. Hadjarian

Inventor： Jianping Zhang ,  Brent M. Han ,  Ali R. Hadjarian

IPC: G06F17/30

CPC classification number: G06N5/025

Abstract: Systems, methods and computer program products for the ranking of a target data set based on learned rules are disclosed. One embodiment is a method that includes generating a learned rule set from a training data record set, creating at least one prototype for each rule in the learned rule set to generate a prototype set, and ranking the target data record set using learned rule set and the prototype set. The generating of a learned rule set includes dividing the training data record set to a positive class and a negative class, and deriving the learned rule set for the positive class. Learning of rules includes deriving the most general projected rules with respect to remaining training data and then refining those rules, eventually selecting the best rules using an F-measure.

Abstract translation: 公开了基于学习规则对目标数据集进行排序的系统，方法和计算机程序产品。 一个实施例是一种方法，其包括从训练数据记录集生成学习规则集，为学习规则集中的每个规则创建至少一个原型以生成原型集，并且使用学习规则集对目标数据记录集进行排序，以及 原型集。 学习规则集的生成包括将训练数据记录集划分为正类和负类，并且导出积极类的学习规则集。 规则的学习包括获得关于剩余训练数据的最一般的预测规则，然后改进这些规则，最终使用F度量来选择最佳规则。