公开(公告)号：US20160063389A1

公开(公告)日：2016-03-03

申请号：US14473717

申请日：2014-08-29

Applicant: salesforce.com, inc.

Inventor： Matthew D. Fuchs ,  Arun Jagota

IPC: G06N7/00 ,  G06N99/00

CPC classification number: G06N5/02 ,  G06F17/30292 ,  G06N7/005 ,  G06N99/005

Abstract: The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set.

Abstract translation: 所公开的技术涉及用于分配贝叶斯分类器的特征集合的方法，找到使分类处理更快更准确的数据分区，同时发现并考虑数据集中的特征集合之间的特征依赖性。 它涉及跨共享特征子集的所有元组的类标签的计算类熵分数，并且按照类标签的非递减熵分数的顺序排列元组，以及构建提供预测精度最高改进的数据分区 用于数据集。 还公开了一种用于分割批量计算中的特征记录的完整集合的方法，计算增加的​​预测能力; 并且还涉及从单例分区开始，并且使用迭代过程构造提供数据集的预测精度最高改进的数据分区。

公开(公告)号：US09349101B2

公开(公告)日：2016-05-24

申请号：US14473717

申请日：2014-08-29

Applicant: salesforce.com, inc.

Inventor： Matthew D. Fuchs ,  Arun Jagota

IPC: G06N7/00 ,  G06N99/00

CPC classification number: G06N5/02 ,  G06F17/30292 ,  G06N7/005 ,  G06N99/005

Abstract: The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set.

Abstract translation: 所公开的技术涉及用于分配贝叶斯分类器的特征集合的方法，找到使分类处理更快更准确的数据分区，同时发现并考虑数据集中的特征集合之间的特征依赖性。 它涉及跨共享特征子集的所有元组的类标签的计算类熵分数，并且按照类标签的非递减熵分数的顺序排列元组，以及构建提供预测精度最高改进的数据分区 用于数据集。 还公开了一种用于分割批量计算中的特征记录的完整集合的方法，计算增加的​​预测能力; 并且还涉及从单例分区开始，并且使用迭代过程构造提供数据集的预测精度最高改进的数据分区。