Defect prediction method and apparatus

• US 10,068,176 B2
• Filed: 12/31/2014
• Issued: 09/04/2018
• Est. Priority Date: 02/28/2013
• Status: Active Grant

• Current Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Inventors: Chan, Wun Wah Edmond, Pan, Lujia
• Primary Examiner(s): Vincent, David R
• Application Number:
  US14/587,724
• Publication Number:
  US 20150112903A1
• Time in Patent Office: 1,343 Days
• Field of Search: 706 12, 706 45
• CPC Class Codes: G06F 16/9027 Trees
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

Embodiments of the present invention disclose a defect prediction method and apparatus, which relate to the data processing field, and implement accurate and quick locating of a defect in a faulty product. A specific solution is as follows: selecting a training attribute set from a pre-stored product fault record according to a target attribute, and combining the target attribute and the training attribute set into a training set, where the target attribute is a defect attribute of a historical faulty product; generating a classifier set according to the training set, where the classifier set includes at least two tree classifiers; and predicting a defect of a faulty product by using the classifier set as a prediction model. The present invention is used in a process of predicting a defect of a faulty product.

Claims

1. A defect prediction method, comprising:

selecting, by a processing device, a training attribute set from a pre-stored product fault record according to a target attribute, and combining the target attribute and the training attribute set into a training set, wherein the target attribute is a defect attribute of a historical faulty product;

generating, by the processing device, a classifier set according to the training set, wherein the classifier set comprises at least two tree classifiers;

predicting, by the processing device, a defect of a faulty product by using the classifier set as a prediction model;

acquiring, by the processing device, an error rate of generated K−

1 tree classifiers when a (K−

1)^th tree classifier is generated;

acquiring, by the processing device, an error rate of generated K tree classifiers when a K^th tree classifier is generated, so that when a difference between the error rate of the K tree classifiers and the error rate of the K−

1 tree classifiers is less than a preset threshold, the K tree classifiers are combined to generate the classifier set, wherein K is an integer less than or equal to N, wherein N is an integer greater than or equal to 2;

generating, by the processing device, a prediction result in accordance with the predicted defect; and

notifying a user of the prediction result.

2. The defect prediction method according to claim 1, wherein:

the training set comprises M training units, wherein M is an integer greater than or equal to 1, and each training unit comprises one target attribute and one training attribute set; and

generating a classifier set according to the training set comprises;

selecting a first training subset from the training set,

generating, according to a preset policy, a first tree classifier corresponding to the first training subset,

selecting a second training subset from the training set,

generating, according to the preset policy, a second tree classifier corresponding to the second training subset,

selecting an N^th training subset from the training set, wherein the N^th training subset comprises M′

training units, and M′

is less than or equal to M,

generating, according to the preset policy, an N^th tree classifier corresponding to the N^th training subset, wherein N is an integer greater than or equal to 2, and

combining N tree classifiers to generate the classifier set.

3. The defect prediction method according to claim 1, wherein acquiring an error rate of generated K tree classifiers when a K^th tree classifier is generated comprises:

selecting a first type tree classifier from the classifier set according to a first training unit;

generating a first prediction label of the first training unit according to the first type tree classifier;

selecting a second type tree classifier from the classifier set according to a second training unit;

generating a second prediction label of the second training unit according to the second type tree classifier;

selecting an M^th type tree classifier from the classifier set according to an M^th training unit, wherein the M^th type tree classifier is a classifier set that does not use the M^th training unit to generate a tree classifier, and M is the number of training units comprised in the training set;

generating an M^th prediction label of the M^th training unit according to the M^th type tree classifier; and

acquiring the error rate of the generated K tree classifiers according to M prediction labels.

4. The defect prediction method according to claim 3, wherein generating an M^th prediction label of the M^th training unit according to the M^th type tree classifier comprises:

generating the M^th prediction label according to

5. The defect prediction method according to claim 4, wherein acquiring the error rate of the generated K tree classifiers according to M prediction labels comprises:

acquiring the error rate of the generated K tree classifiers according to

8. The defect prediction method according to claim 2, wherein the preset policy comprises a decision tree algorithm.

6. A defect prediction method, comprising:

selecting, by a processing device, a training attribute set from a pre-stored product fault record according to a target attribute, and combining the target attribute and the training attribute set into a training set, wherein the target attribute is a defect attribute of a historical faulty product;

generating, by the processing device, a classifier set according to the training set, wherein the classifier set comprises at least two tree classifiers;

predicting, by the processing device, a defect of a faulty product by using the classifier set as a prediction model;

wherein the training set comprises M training units, wherein M is an integer greater than or equal to 1, and each training unit comprises one target attribute and one training attribute set;

wherein generating a classifier set according to the training set comprises;

selecting a first training subset from the training set,

generating, according to a preset policy, a first tree classifier corresponding to the first training subset,

selecting a second training subset from the training set,

generating, according to the preset policy, a second tree classifier corresponding to the second training subset,

selecting an N^th training subset from the training set, wherein the N^th training subset comprises M′

training units, and M′

is less than or equal to M,

generating, according to the preset policy, an N^th tree classifier corresponding to the N^th training subset, wherein N is an integer greater than or equal to 2, and

combining N tree classifiers to generate the classifier set;

wherein after generating, according to the preset policy, an N^th tree classifier corresponding to the N^th training subset, the method further comprises;

selecting an N′

^th training subset from the training set, wherein an intersection set of the N′

^th training subset and the N^th training subset is empty, and the N′

^th training subset comprises at least one training unit,

acquiring a false prediction rate of the N^th tree classifier according to the N′

^th training subset, and

acquiring a weight of the N^th tree classifier according to the false prediction rate of the N^th tree classifier;

generating, by the processing device, a prediction result in accordance with the predicted defect; and

notifying a user of the prediction result.

7. The defect prediction method according to claim 6 wherein predicting a defect of a faulty product by using the classifier set as a prediction model comprises:

collecting statistics about attribute information of the faulty product;

predicting the defect of the faulty product by using the classifier set as a prediction model and according to the attribute information, so as to obtain a classification label set; and

acquiring a trust value of each classification label in the classification label set according to the classifier set and a weight of each tree classifier in the classifier set.

9. A defect prediction apparatus, comprising:

a processor;

memory coupled to the processor, the memory comprising instructions that, when executed by the processor, cause the defect prediction apparatus to;

select a training attribute set from a pre-stored product fault record according to a target attribute, and combine the target attribute and the training attribute set into a training set, wherein the target attribute is a defect attribute of a historical faulty product,

generate a classifier set according to the training set, wherein the classifier set comprises at least two tree classifiers,

predict a defect of a faulty product by using the classifier set as a prediction model,

acquire an error rate of generated K−

1 tree classifiers when a (K−

1)^th tree classifier is generated,

acquire an error rate of generated K tree classifiers when a K^th tree classifier is generated, so that when a difference between the error rate of the K tree classifiers and the error rate of the K−

1 tree classifiers is less than a preset threshold, the K tree classifiers are combined to generate the classifier set, wherein K is an integer less than or equal to N, wherein N is an integer greater than or equal to 2;

generate a prediction result in accordance with the predicted defect; and

notify a user of the prediction result.

10. The defect prediction apparatus according to claim 9, wherein:

the training set comprises M training units, wherein M is an integer greater than or equal to 1, and each training unit comprises one target attribute and one training attribute set; and

the memory further comprises instructions that, when executed by the processor, cause the defect prediction apparatus to;

select a first training subset from the training set,

generate, according to a preset policy, a first tree classifier corresponding to the selected first training subset,

select a second training subset from the training set,

generate, according to the preset policy, a second tree classifier corresponding to the selected second training subset,

select an N^th training subset from the training set, wherein the N^th training subset comprises M′

training units, and M′

is less than or equal to M, and

generate, according to the preset policy, an N^th tree classifier corresponding to the selected N^th training subset, wherein N is an integer greater than or equal to 2, and

combine N tree classifiers to generate the classifier set.

11. The defect prediction apparatus according to claim 10, wherein the memory further comprises instructions that, when executed by the processor, cause the defect prediction apparatus to:

select a first type tree classifier from the classifier set according to a first training unit;

generate a first prediction label of the first training unit according to the selected first type tree classifier,

select a second type tree classifier from the classifier set according to a second training unit,

generate a second prediction label of the second training unit according to the selected second type tree classifier,

select an M^th type tree classifier from the classifier set according to an M^th training unit, wherein the M^th type tree classifier is a classifier set that does not use the M^th training unit to generate a tree classifier, and M is the number of training units comprised in the training set, and

generate an M^th prediction label of the M^th training unit according to the selected M^th type tree classifier; and

acquire the error rate of the generated K tree classifiers according to M prediction labels that are generated by the generating submodule.

12. The defect prediction apparatus according to claim 11, wherein the memory further comprises instructions that, when executed by the processor, cause the defect prediction apparatus to:

generate the M^th prediction label according to

13. The defect prediction apparatus according to claim 12, wherein the memory further comprises instructions that, when executed by the processor, cause the defect prediction apparatus to:

acquire, according to

16. The defect prediction apparatus according to claim 10, wherein the preset policy comprises a decision tree algorithm.

14. A defect prediction apparatus, comprising:

a processor;

memory coupled to the processor, the memory comprising instructions that, when executed by the processor, cause the defect prediction apparatus to;

select a training attribute set from a pre-stored product fault record according to a target attribute, and combine the target attribute and the training attribute set into a training set, wherein the target attribute is a defect attribute of a historical faulty product,

generate a classifier set according to the training set, wherein the classifier set comprises at least two tree classifiers, and

predict a defect of a faulty product by using the classifier set as a prediction model;

wherein the training set comprises M training units, wherein M is an integer greater than or equal to 1, and each training unit comprises one target attribute and one training attribute set; and

the memory further comprises instructions that, when executed by the processor, cause the defect prediction apparatus to;

select a first training subset from the training set,

generate, according to a preset policy, a first tree classifier corresponding to the selected first training subset,

select a second training subset from the training set,

generate, according to the preset policy, a second tree classifier corresponding to the selected second training subset,

select an N^th training subset from the training set, wherein the N^th training subset comprises M′

training units, and M′

is less than or equal to M,

generate, according to the preset policy, an N^th tree classifier corresponding to the selected N^th training subset, wherein N is an integer greater than or equal to 2,

combine N tree classifiers to generate the classifier set,

select an N′

^th training subset from the training set after generation of an N^th tree classifier corresponding to the N^th training subset, wherein an intersection set of the N′

^th training subset and the N^th training subset is empty, and the N′

^th training subset comprises at least one training unit,

acquire a false prediction rate of the N^th tree classifier according to the N′

^th training subset, and

acquire a weight of the N^th tree classifier according to the acquired false prediction rate of the N^th tree classifier;

generate a prediction result in accordance with the predicted defect; and

notify a user of the prediction result.

15. The defect prediction apparatus according to claim 14, wherein the memory further comprises instructions that, when executed by the processor, cause the defect prediction apparatus to:

collect statistics about attribute information of the faulty product;

predict the defect of the faulty product by using the classifier set as a prediction model and according to the attribute information, so as to obtain a classification label set; and

acquire a trust value of each classification label in the classification label set according to the classifier set and a weight of each tree classifier in the classifier set.