Non-transitory computer-readable recording medium for storing model generation program, model generation method, and model generation device

Patent metadata

Field	Value
Publication number	US-2023196109-A1
Application number	US-202318172419-A
Country	US
Kind code	A1
Filing date	Feb 22, 2023
Priority date	Aug 31, 2020
Publication date	Jun 22, 2023
Grant date	—

Abstract

A non-transitory computer-readable recording medium storing a model generation program for causing a computer to perform processing including: changing first data and generating a plurality of pieces of data; calculating a plurality of values indicating a distance between the first data and each of the plurality of pieces of data; determining whether or not a value indicating uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold based on the plurality of values; and in a case where the value indicating the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as explanatory variables.

First claim

What is claimed is:

1 . A non-transitory computer-readable recording medium storing a model generation program for causing a computer to perform processing, the processing comprising:
changing first data and generating a plurality of pieces of data; calculating a plurality of values that indicates a distance between the first data and each of the plurality of pieces of data; determining whether or not a value that indicates uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold on a basis of the plurality of values; and in a case where the value that indicates the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model by using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as an explanatory variable.

2 . The non-transitory computer-readable recording medium according to claim 1 , wherein the calculating includes calculating the plurality of values that indicates the distance on a basis of a graph kernel function.

3 . The non-transitory computer-readable recording medium according to claim 1 , wherein
the determining includes further determining whether or not a difference between a number of first values and a number of second values included in the result obtained by inputting the plurality of pieces of data into the machine learning model is equal to or smaller than a threshold, and the generating the linear regression model includes executing the generation of the linear regression model in a case where the difference between the number of the first values and the number of the second values is determined to be equal to or smaller than the threshold.

4 . The non-transitory computer-readable recording medium according to claim 1 , wherein
the generating the plurality of pieces of data includes generating new data and adding the new data to the plurality of pieces of data in a case where the value that indicates the uniformity is smaller than the threshold, and the determining includes further determining whether or not a value that indicates uniformity of distribution of a distance between the first data and each of the plurality of pieces of data to which the new data is added is equal to or greater than a threshold.

5 . The non-transitory computer-readable recording medium according to claim 1 , wherein the calculating includes changing a distance function used to calculate the plurality of values that indicates the distance in a case where the value that indicates the uniformity is smaller than the threshold.

6 . The non-transitory computer-readable recording medium according to claim 1 , the processing further including: calculating a partial regression coefficient of the linear regression model as a contribution level of a feature included in the plurality of pieces of data to an output of the machine learning model.

7 . A model generation method implemented by a computer, the model generation method comprising:
changing, in processor circuitry of the computer, first data and generating a plurality of pieces of data; calculating, in the processor circuitry of the computer, a plurality of values that indicates a distance between the first data and each of the plurality of pieces of data; determining, in the processor circuitry of the computer, whether or not a value that indicates uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold on a basis of the plurality of values; and in a case where the value that indicates the uniformity is determined to be equal to or greater than the threshold, generating, in the processor circuitry of the computer, a linear regression model by using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as an explanatory variable.

8 . The model generation method according to claim 7 , wherein the calculating includes calculating the plurality of values that indicates the distance on a basis of a graph kernel function.

9 . The model generation method according to claim 7 , wherein
the determining includes further determining whether or not a difference between a number of first values and a number of second values included in the result obtained by inputting the plurality of pieces of data into the machine learning model is equal to or smaller than a threshold, and the generating the linear regression model includes executing the generation of the linear regression model in a case where the difference between the number of the first values and the number of the second values is determined to be equal to or smaller than the threshold.

10 . The model generation method according to claim 7 , wherein
the generating the plurality of pieces of data includes generating new data and adding the new data to the plurality of pieces of data in a case where the value that indicates the uniformity is smaller than the threshold, and the determining includes further determining whether or not a value that indicates uniformity of distribution of a distance between the first data and each of the plurality of pieces of data to which the new data is added is equal to or greater than a threshold.

11 . The model generation method according to claim 7 , wherein the calculating includes changing a distance function used to calculate the plurality of values that indicates the distance in a case where the value that indicates the uniformity is smaller than the threshold.

12 . The model generation method according to claim 7 , the model generation method further comprising: calculating a partial regression coefficient of the linear regression model as a contribution level of a feature included in the plurality of pieces of data to an output of the machine learning model.

13 . A model generation device comprising
a memory; and processor circuitry coupled to the memory, the processor circuitry being configured to perform processing including:
changing first data and generating a plurality of pieces of data;
calculating a plurality of values that indicates a distance between the first data and each of the plurality of pieces of data;
determining whether or not a value that indicates uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold on a basis of the plurality of values; and
in a case where the value that indicates the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model by using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as an explanatory variable.

14 . The model generation device according to claim 13 , wherein the calculating includes calculating the plurality of values that indicates the distance on a basis of a graph kernel function.

15 . The model generation device according to claim 13 , wherein
the determining includes further determining whether or not a difference between a number of first values and a number of second values included in the result obtained by inputting the plurality of pieces of data into the machine learning model is equal to or smaller than a threshold, and the generating the linear regression model includes

Assignees

• Fujitsu Ltd

Inventors

• Todoriki Masaru
• Shingu Masafumi
• Maruhashi Koji

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• G06N3/04 Primary

  Architecture, e.g. interconnection topology · CPC title

• G06F40/56

  Natural language generation · CPC title

• G06N3/082

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06N20/00

  Machine learning · CPC title