# US 2020/0401943 A1 · Model learning apparatus, model learning method, and program

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- Number: US 2020/0401943 A1
- Document ID: US20200401943A1
- Status: Published application
- Kind: A1
- Application number: 16/969,145
- Filed: 2019-02-13
- Published: 2020-12-24
- Earliest claimed: 2018-02-13
- Assignees: Nippon Telegraph and Telephone Corporation
- Applicants: Nippon Telegraph and Telephone Corporation
- Inventors: Yuta KAWACHI; Yuma KOIZUMI; Noboru HARADA
- Web page: https://patentgrep.com/patent/US20200401943A1

## CPC

- G06N 20/00: Machine learning
- G06F 17/16: Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)}
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F 18/2431: Multiple classes
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

There is provided a model learning technique for learning a model which performs classification into three values by model learning using an AUC optimization criterion. A model learning unit is included which learns a parameter ψ{circumflex over ( )} of a model by using a learning data set based on a criterion which uses a predetermined AUC value, the learning data set being defined using normal data generated from sound observed in a normal state and abnormal data generated from sound observed in an abnormal state, and the AUC value is defined from a difference between an abnormality degree of the normal data and an abnormality degree of the abnormal data using a two-stage step function T(x).

## Claims

### Claim at position 1

-8. (canceled)

### Claim 9

A computer-implemented method for model learning for three-valued classification, the method comprising:
generating normal data for learning positive results;
generating abnormal data for learning negative results;
generating a learning data set based on the normal data and the abnormal data;
learning a classification model using the generated learning data set based on a predetermined area under-the-receiver-operating-characteristic curve (AUC) values, wherein the AUC values are based at least on a difference between a first abnormality degree of the normal data and a second abnormality degree of the abnormal data using a two-stage step operation.

### Claim 10 (depends on 9)

The computer-implemented method of claim 9, wherein the AUC values are based on an average of a combination of the two-stage step operation and the difference between the first abnormality degree of the normal data and the second abnormality degree of the abnormal data, and wherein the two-stage step operation relates to states of normal, abnormal, and indistinguishable

### Claim 11 (depends on 10)

The computer-implemented method of claim 10, wherein the two-stage step operations are differentiable.

### Claim 12 (depends on 9)

The computer-implemented method of claim 9, wherein the learning of a parameter of a classification model uses an AUC optimization criterion with the two-stage step calculation.

### Claim 13 (depends on 9)

The computer-implemented method of claim 9, the method further comprising:
receiving normal data, the normal data representing data in a normal status;
receiving abnormal sound data, the abnormal data representing data in an abnormal status, the abnormal sound data and the normal sound data being distinct;
generating the normal data based on the normal sound data using vector conversion; and
generating the abnormal data based on the abnormal sound data using vector conversion.

### Claim 14 (depends on 9)

The computer-implemented method of claim 9, wherein, based on the two-stage step calculation, the classification model provides a three-valued classification, the three-valued classification includes:
a normal class,
an abnormal class, and
an indistinguishable class.

### Claim 15 (depends on 14)

The computer-implemented method of claim 14, wherein the normal data represent normal sound data indicating sound of an object operating in a normal status, wherein the abnormal data represent abnormal sound data indicating sound the object operating in an abnormal status, and wherein the indistinguishable class represents an escalation status requiring visual inspections of the object.

### Claim 16

A system for a three-valued classification, the system comprising:
a processor; and
a memory storing computer-executable instructions that when executed by the processor cause the system to:
generate normal data for learning positive results;
generate abnormal data for learning negative results;
generate a learning data set based on the normal data and the abnormal data;
learn a classification model using the generated learning data set based on a predetermined area under-the-receiver-operating-characteristic curve (AUC) values, wherein the AUC values are based at least on a difference between a first abnormality degree of the normal data and a second abnormality degree of the abnormal data using a two-stage step operation.

### Claim 17 (depends on 16)

The computer-implemented method of claim 16, wherein the AUC values are based on an average of a combination of the two-stage step operation and the difference between the first abnormality degree of the normal data and the second abnormality degree of the abnormal data, and wherein the two-stage step operation relates to states of normal, abnormal, and indistinguishable.

### Claim 18 (depends on 17)

The system of claim 17, wherein the two-stage step operations are differentiable.

### Claim 19 (depends on 16)

The system of claim 16, wherein the learning of a parameter ψ{circumflex over ( )} of a classification model uses an AUC optimization criterion with the two-stage step calculation.

### Claim 20 (depends on 16)

The system of claim 16, the computer-executable instructions when executed further causing the system to:
receive normal data, the normal data representing data in a normal status;
receive abnormal sound data, the abnormal data representing data in an abnormal status, the abnormal sound data and the normal sound data being distinct;
generate the normal data based on the normal sound data using vector conversion; and
generate the abnormal data based on the abnormal sound data using vector conversion.

### Claim 21 (depends on 16)

The system of claim 16, wherein, based on the two-stage step calculation, the classification model provides a three-valued classification, the three-valued classification includes:
a normal class,
an abnormal class, and
an indistinguishable class.

### Claim 22 (depends on 21)

The system of claim 21, wherein the normal data represent normal sound data indicating sound of an object operating in a normal status, wherein the abnormal data represent abnormal sound data indicating sound the object operating in an abnormal status, and wherein the indistinguishable class represents an escalation status requiring visual inspections of the object.

### Claim 23

A computer-readable non-transitory recording medium storing computer-executable instructions that when executed by a processor cause a computer system to:
generate normal data for learning positive results;
generate abnormal data for learning negative results;
generate a learning data set based on the normal data and the abnormal data;
learn a classification model using the generated learning data set based on a predetermined area under-the-receiver-operating-characteristic curve (AUC) values, wherein the AUC values are based at least on a difference between a first abnormality degree of the normal data and a second abnormality degree of the abnormal data using a two-stage step operation.

### Claim 24 (depends on 23)

The computer-readable non-transitory recording medium of claim 23, wherein the AUC values are based on an average of a combination of the two-stage step operation and the difference between the first abnormality degree of the normal data and the second abnormality degree of the abnormal data, and wherein the two-stage step operation relates to states of normal, abnormal, and indistinguishable.

### Claim 25 (depends on 23)

The computer-readable non-transitory recording medium of claim 23, wherein the two-stage step operations are differentiable.

### Claim 26 (depends on 23)

The computer-readable non-transitory recording medium of claim 23, wherein the learning of a parameter of a classification model uses an AUC optimization criterion with the two-stage step calculation.

### Claim 27 (depends on 23)

The computer-readable non-transitory recording medium of claim 23, the computer-executable instructions when executed further causing the system to:
receive normal data, the normal data representing data in a normal status;
receive abnormal sound data, the abnormal data representing data in an abnormal status, the abnormal sound data and the normal sound data being distinct;
generate the normal data based on the normal sound data using vector conversion; and
generate the abnormal data based on the abnormal sound data using vector conversion.

### Claim 28 (depends on 23)

The computer-readable non-transitory recording medium of claim 23, wherein, based on the two-stage step calculation, the classification model provides a three-valued classification, the three-valued classification includes:
a normal class,
an abnormal class, and
an indistinguishable class.