Data meta-scaling apparatus and method for continuous learning

Patent metadata

Field	Value
Publication number	US-2018189655-A1
Application number	US-201715854387-A
Country	US
Kind code	A1
Filing date	Dec 26, 2017
Priority date	Jan 3, 2017
Publication date	Jul 5, 2018
Grant date	—

Abstract

Provided is a data meta-scaling method. The data meta-scaling method optimizes an abbreviation criterion for abbreviating data through continuous knowledge augmentation in various dimensions which enable expression of data in a process of performing machine learning.

First claim

What is claimed is:

1 . A data meta-scaling method for continuous learning, the data meta-scaling method comprising:
setting, by a processor, abbreviation criterion information which defines a rule for abbreviating input data to be expressed in another attribute, learning criterion information which defines a rule for limiting learning on the abbreviation data and a rule for evaluating learning performance, and knowledge augmentation criterion information which defines a rule for optimizing the abbreviation criterion information; abbreviating, by the processor, the input data to abbreviation data, based on the abbreviation criterion information; performing, by the processor, learning on the abbreviation data to generate a learning model, based on the learning criterion information; evaluating, by the processor, performance of the learning model to determine suitability of the abbreviation data, based on the learning criterion information; and performing, by the processor, knowledge augmentation for updating the abbreviation criterion information according to a result of the suitability determination, based on the knowledge augmentation criterion information.

2 . The data meta-scaling method of claim 1 , wherein the setting comprises setting the abbreviation criterion information which defines a rule for abbreviating the input data expressed as a plurality of attributes to be expressed as at least one of the plurality of attributes.

3 . The data meta-scaling method of claim 1 , wherein the setting comprises, when the input data is expressed as a plurality of attributes, setting the abbreviation criterion information which includes information representing a data dimension defining one of the plurality of attributes, information representing a window defining a unit of sampling of the input data, information representing a kind of the window, information representing a size of the window, and information representing a criterion for selecting a representative value in the window.

4 . The data meta-scaling method of claim 1 , wherein the setting comprises setting the learning criterion information which includes information representing a kind of the input data, information representing a condition of learning reliability for evaluating performance of the learning model, information representing a method of calculating the learning reliability, and information representing an early stop condition of learning which limits number of repetitions of the learning on the abbreviation data.

5 . The data meta-scaling method of claim 1 , wherein the setting comprises setting the knowledge augmentation criterion information which includes information representing number of changes of the abbreviation criterion information, information representing a change factor of the abbreviation criterion information, information representing a change range of the change factor, and information representing number of accumulations of a learning history generated in a process of performing learning on the abbreviation data.

6 . The data meta-scaling method of claim 5 , wherein the change factor is information associated with a window defining a unit of sampling of the input data.

7 . The data meta-scaling method of claim 6 , wherein the information associated with the window comprises pieces of information representing a size of the window and an interval between windows.

8 . The data meta-scaling method of claim 1 , wherein the abbreviating comprises, when the input data is expressed as a plurality of attributes and the plurality of attributes are defined as a plurality of data dimensions, abbreviating the input data to abbreviation data through one of a first process of sampling the input data as a representative value of the input data in each of the plurality of data dimensions, a second process of changing the input data to at least one data dimension selected from among the plurality of data dimensions, and a third process including a combination of the first process and the second process.

9 . The data meta-scaling method of claim 8 , wherein the first process comprises:
a process of periodically sampling the input data as the representative value of the input data; a process of aperiodically sampling the input data as the representative value of the input data; a fixed window-based sampling process of, in a state where a plurality of windows defining a unit of sampling of the input data do not overlap each other, selecting the representative value in each of the plurality of windows; and a moving window-based sampling process of, in a state where the plurality of windows overlap each other, selecting the representative value in each of the plurality of windows.

10 . The data meta-scaling method of claim 1 , wherein the performing of the knowledge augmentation comprises:
when learning reliability calculated for evaluating the performance of the learning model does not satisfy a condition prescribed in the rule, defined in the learning criterion information, for evaluating the learning performance, changing the abbreviation criterion information according to information representing a change factor, defined in the knowledge augmentation criterion information, of the abbreviation criterion information and a change range of the change factor; and when performance of a learning model generated by performing learning on the abbreviation data abbreviated based on the changed abbreviation criterion information satisfies a condition prescribed in the learning criterion information, updating the changed abbreviation criterion information to optimal abbreviation criterion information.

11 . A data meta-scaling apparatus for continuous learning, the data meta-scaling apparatus comprising:
a meta-optimizer setting abbreviation criterion information which defines a rule for abbreviating input data to be expressed in another attribute, learning criterion information which defines a rule for limiting learning on the abbreviation data and a rule for evaluating learning performance, and knowledge augmentation criterion information which defines a rule for optimizing the abbreviation criterion information; an abbreviator abbreviating the input data to abbreviation data, based on the abbreviation criterion information; a learning machine performing learning on the abbreviation data to generate a learning model, based on the learning criterion information; and an evaluator evaluating performance of the learning model to determine suitability of the abbreviation data, based on the learning criterion information, wherein the meta-optimizer performs knowledge augmentation for updating the abbreviation criterion information according to a result of the suitability determination, based on the knowledge augmentation criterion information.

12 . The data meta-scaling apparatus of claim 11 , wherein the meta-optimizer sets the abbreviation criterion information which defines a rule for abbreviating the input data expressed as a plurality of attributes to be expressed as at least one of the plurality of attributes.

13 . The data meta-scaling apparatus of claim 11 , wherein when the input data is expressed as a plurality of attributes, the meta-optimizer sets the abbreviation criterion information which includes information representing a data dimension defining one of the plurality of attributes, information representing a window defining a unit of sampling of the input data, information representing a kind of the window, information representing a size of the window, and information representing a criterion for selecting a representative value in the window.

Assignees

• Electronics & Telecommunications Res Inst

Inventors

• Oh Se Won
• Lee Yeon Hee
• Bae Ji Hoon
• Kang Hyun Joong
• Kwon Soon Hyun
• Kim Kwi Hoon
• Kim Young Min
• Kim Eun Joo
• Kim Hyun Jae
• Park Hong Kyu
• Yu Jae Hak
• Lee Ho Sung
• Cho Seong Ik
• Kim Nae Soo
• Kim Sun Jin
• Pyo Cheol Sig

Classifications

• G06F15/18

  Physics · mapped topic

• G06F17/17

  Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method ({G06F17/18 takes precedence } ; interpolation for numerical control G05B19/18) · CPC title

• G06F7/023

  adaptive, e.g. self learning · CPC title

• G06N5/02 Primary

  Knowledge representation; Symbolic representation · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title