Prediction model construction device, prediction model construction method and prediction model construction program recording medium

Patent metadata

Field	Value
Publication number	US-2021019636-A1
Application number	US-201817043309-A
Country	US
Kind code	A1
Filing date	May 11, 2018
Priority date	May 11, 2018
Publication date	Jan 21, 2021
Grant date	—

Abstract

This prediction model preparation device is provided with: a calculation means which calculates, from a datum in which a sample and a label are associated with each other, an importance level according to the difference between a first possibility that an event influencing the sample occurs in a source domain and a second possibility that the event occurs in a target domain; and a preparation means which constructs prepares a prediction model relating to the target domain by calculating association between the sample and the label included in the datum to which the importance level is added.

First claim

What is claimed is:

1 . A prediction model preparation device comprising:
a calculation unit configured to calculate, from a datum in which a sample and a label are associated with each other, an importance level according to a difference between a first possibility that an event influencing the sample occurs in a source domain and a second possibility that the event occurs in a target domain; and a preparation unit configured to prepare a prediction model relating to the target domain by calculating association between the sample and the label included in the datum to which the importance level is added.

2 . The prediction model preparation device as claimed in claim 1 , wherein the calculation unit comprises:
an intra-data attribute distribution estimation unit configured to estimate an attribute distribution in each source datum based on source data of the source domain and a first distribution of attribute information in the source domain; an intra-attribute domain distribution estimation unit configured to estimate a domain distribution in each attribute based on the first distribution of the attribute information in the source domain and a second distribution of attribute information in the target domain; and a domain adaptation unit configured to estimate a distribution of the target domain in each target datum based on the estimated attribute distribution in each source datum and the domain distribution in each attribute and to calculate, as the importance level, a conversion parameter for converting the source datum so as to increase similarity in data distribution between the source domain and the target domain.

3 . The prediction model preparation device as claimed in claim 2 , wherein the domain adaptation unit is configured to perform sample weighting as a data conversion method.

4 . A prediction model preparation method, which is executed by an information processing device, comprising:
calculating, from a datum in which a sample and a label are associated with each other, an importance level according to a difference between a first possibility that an event influencing the sample occurs in a source domain and a second possibility that the event occurs in a target domain; and preparing a prediction model relating to the target domain by calculating association between the sample and the label included in the datum to which the importance level is added.

5 . The prediction model preparation method as claimed in claim 4 , wherein the calculating comprises:
estimating an attribute distribution in each source datum based on source data of the source domain and a first distribution of attribute information in the source domain; estimating a domain distribution in each attribute based on the first distribution of the attribute information in the source domain and a second distribution of attribute information in the target domain; and estimating a distribution of the target domain in each target datum based on the estimated attribute distribution in each source datum and the domain distribution in each attribute and calculating, as the importance level, a conversion parameter for converting the source datum so as to increase similarity in data distribution between the source domain and the target domain.

6 . The prediction model preparation method as claimed in claim 5 , wherein the calculating the conversion parameter comprises performing sample weighting as a data conversion method.

7 . A non-transitory computer readable recording medium recording a prediction model preparation program which causes a computer to execute:
a calculation step of calculating, from a datum in which a sample and a label are associated with each other, an importance level according to a difference between a first possibility that an event influencing the sample occurs in a source domain and a second possibility that the event occurs in a target domain; and a preparation step of preparing a prediction model relating to the target domain by calculating association between the sample and the label included in the datum to which the importance level is added.

8 . The non-transitory computer readable recording medium as claimed in claim 7 , wherein the calculation step causes the computer to execute:
an intra-data attribute distribution estimation step of estimating an attribute distribution in each source datum based on source data of the source domain and a first distribution of attribute information in the source domain; an intra-attribute domain distribution estimation step of estimating a domain distribution in each attribute based on the first distribution of the attribute information in the source domain and a second distribution of attribute information in the target domain; and a domain adaptation step of estimating a distribution of the target domain in each target datum based on the estimated attribute distribution in each source datum and the domain distribution in each attribute and of calculating, as the importance level, a conversion parameter for converting the source datum so as to increase similarity in data distribution between the source domain and the target domain.

9 . The non-transitory computer readable recording medium as claimed in claim 8 , wherein the domain adaptation step performs sample weighting as a data conversion method.

Assignees

• Nec Corp

Inventors

• Ishii Masato
• Takenouchi Takashi
• Sugiyama Masashi

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06T7/00

  Image analysis · CPC title

• G06N5/02 Primary

  Knowledge representation; Symbolic representation · CPC title

• G06N7/005

  Physics · mapped topic