Feature-set augmentation using knowledge engine

Patent metadata

Field	Value
Publication number	US-2017337486-A1
Application number	US-201615157138-A
Country	US
Kind code	A1
Filing date	May 17, 2016
Priority date	May 17, 2016
Publication date	Nov 23, 2017
Grant date	—

Abstract

A method includes receiving an original feature-set for training a machine learning system, the feature-set including multiple records each having a set of original features with original feature values and a result, querying a knowledge base based on the set of original features, receiving a set of knowledge features with knowledge feature values responsive to the querying of the knowledge base, generating a first augmented feature-set that includes the multiple records of the original feature set and the knowledge features for the multiple records, and training the machine learning system based on the first augmented feature-set.

First claim

What is claimed is:

1 . A method comprising:
receiving an original feature-set for training a machine learning system, the feature-set including multiple records each having a set of original features with original feature values and a result; querying a knowledge base based on the set of original features; receiving a set of knowledge features with knowledge feature values responsive to the querying of the knowledge base; generating a first augmented feature-set that includes the multiple records of the original feature set and the knowledge features for the multiple records; and training the machine learning system based on the first augmented feature-set.

2 . The method of claim 1 and further comprising combining multiple values of a single feature to create at least one higher level feature having at least two clusters of higher level feature values.

3 . The method of claim 2 and further comprising selecting at least one higher level feature from a number of higher level features for a physical feature for inclusion in the first augmented feature set for training the machine learning system.

4 . The method of claim 2 wherein a feature value of each cluster is a function of a mean or median value of the feature values in the cluster.

5 . The method of claim 1 and further comprising creating high level feature values from mathematically combined knowledge features, or a group of knowledge features.

6 . The method of claim 4 wherein the mathematically combined features comprises a length and width, and wherein the length and width are multiplied to produce an area as the further feature value.

7 . The method of claim 4 wherein the high level feature values comprise numeric or nominal values.

8 . The method of claim 1 wherein the knowledge base comprises a networked knowledge base.

9 . The method of claim 1 wherein multiple feature values are combined into clusters of higher level feature values based on one or more of a Euclidean distance function, a Manhattan distance function, a Cosine distance function, or a Hamming distance function.

10 . The method of claim 1 wherein the knowledge base comprises the Internet, and wherein the original features comprise cellular phone information and the result comprises a carrier churn value.

11 . The method of claim 1 and further comprising providing an interface to select features to include in the augmented feature set.

12 . A non-transitory machine readable storage device having instructions for execution by one or more processors to perform operations comprising:
receiving an original feature-set for training a machine learning system, the feature-set including multiple records each having a set of original features with original feature values and a result; querying a knowledge base based on the set of original features; receiving a set of knowledge features with knowledge feature values responsive to the querying of the knowledge base; generating a first augmented feature-set that includes the multiple records of the original feature set and the knowledge features for the multiple records; and training the machine learning system based on the first augmented feature-set.

13 . The non-transitory machine readable storage device of claim 12 wherein the operations further comprise combining multiple values of a single feature to create at least one higher level feature having at least one cluster of higher level feature values.

14 . The non-transitory machine readable storage device of claim 12 wherein multiple feature values are combined into clusters of higher level feature values based on one or more of a Euclidean distance function, a Manhattan distance function, a Cosine distance function, or a Hamming distance function to produce a further knowledge feature.

15 . The non-transitory machine readable storage device of claim 12 wherein the knowledge base comprises the Internet, and wherein the original features comprise cellular phone information and the result comprises a carrier churn value.

16 . A device comprising:
a processor; and a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising:
receiving an original feature-set for training a machine learning system, the feature-set including multiple records each having a set of original features with original feature values and a result;
querying a knowledge base based on the set of original features;
receiving a set of knowledge features with knowledge feature values responsive to the querying of the knowledge base;
generating a first augmented feature-set that includes the multiple records of the original feature set and the knowledge features for the multiple records; and
training the machine learning system based on the first augmented feature-set.

17 . The device of claim 16 wherein the operations further comprise combining multiple values of a single feature to create at least one higher level feature having at least one cluster of higher level feature values.

18 . The device of claim 17 wherein the multiple feature values are combined into clusters of higher level feature values based on one or more of a Euclidean distance function, a Manhattan distance function, a Cosine distance function, or a Hamming distance function to produce a further knowledge feature.

19 . The device of claim 16 wherein the operations further comprise creating high level feature values from mathematically combined knowledge features, wherein the mathematically combined features comprises a length and width, and wherein the length and width are multiplied to produce an area as the further feature value.

20 . The device of claim 16 wherein the knowledge base comprises the Internet, and wherein the original features comprise cellular phone information and the result comprises a carrier churn value.

Assignees

• Futurewei Technologies Inc

Inventors

• Zang Hui
• Wu Zonghuan

Classifications

• G06F3/0482

  Interaction with lists of selectable items, e.g. menus · CPC title

• G06F3/04842

  Selection of displayed objects or displayed text elements (G06F3/0482 takes precedence) · CPC title

• G06F16/2455

  Query execution · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N5/02

  Knowledge representation; Symbolic representation · CPC title