DATA CLASSIFICATION DEVICE, DATA CLASSIFICATION METHOD, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM

• US 20180018391A1
• Filed: 07/12/2017
• Published: 01/18/2018
• Est. Priority Date: 07/13/2016
• Status: Abandoned Application

• Current Assignee: Yahoo Japan Corporation (A Holdings Corp.)
• Original Assignee: Yahoo Japan Corporation (A Holdings Corp.)
• Inventors: KAJI, Nobuhiro
• Application Number:
  US15/647,527
• Publication Number:
  US 20180018391A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 17/13 Differential equations using digital differential analysers G06F7/64
• CPC Class Codes: G06F 17/16 Matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization matrix transposition G06F7/78
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/045 Combinations of networks

Abstract

A data classification device according to the present application includes a conversion unit, a classification unit, a first learning unit, and a second learning unit. The conversion unit converts input classification target data into a feature vector. The classification unit provides a label to the classification target data on the basis of the feature vector output by the conversion unit. The first learning unit learns conversion processing of the conversion unit, using accumulated data of the input classification target data, as first learning data. The second learning unit learns classification processing of the classification unit, using second learning data in which a label has been provided to data of a same type as the classification target data.

Claims

1. A data classification device comprising:

a conversion unit that converts input classification target data into a feature vector;

a classification unit that provides a label to the classification target data on the basis of the feature vector output by the conversion unit;

a first learning unit that learns conversion processing of the conversion unit, using accumulated data of the input classification target data, as first learning data; and

a second learning unit that learns classification processing of the classification unit, using second learning data in which a label has been provided to data of a same type as the classification target data.

2. The data classification device according to claim 1, wherein

the conversion unit converts the classification target data into vector data as the feature vector by reference to a vector representation table in which a word and a vector are associated, and

the first learning unit updates the vector included in the vector representation table, using the first learning data not including information indicating a positive sample or a negative sample.

3. The data classification device according to claim 2, wherein

the first learning unit updates a first vector associated with a first word included in the classification target data and a second vector associated with a second word related to the first word such that the first vector and the second vector included in the vector representation table have close values.

4. The data classification device according to claim 3, wherein

the second word related to the first word is a word existing within predetermined words from the first word in the classification target data.

5. The data classification device according to claim 3, wherein

the first learning unit calculates a loss function, using the first vector, the second vector, and a third vector associated with a negative sample, and updates the first vector, the second vector, and the third vector, using a partial differential value of the calculated loss function.

6. The data classification device according to claim 1, wherein

the second learning unit updates a classification reference parameter to be used to classify the feature vector output by the conversion unit, on the basis of the second learning data including information indicating a positive sample or a negative sample.

7. The data classification device according to claim 6, wherein

the second learning unit outputs the second learning data to the conversion unit,

the conversion unit converts the second learning data output from the second learning unit into the feature vector, and outputs the feature vector to the second learning unit, and

the second learning unit updates the classification reference parameter on the basis of the feature vector output from the conversion unit and the label provided to the second learning data.

8. The data classification device according to claim 1, wherein

processing by the conversion unit and the classification unit is executed in asynchronization with processing by the first learning unit and the second learning unit.

9. The data classification device according to claim 1, wherein

the first learning data is stored in a first storage unit,

the first learning unit starts learning processing of learning conversion processing of the conversion unit when the first learning data stored in the first storage unit has exceeded a predetermined amount.

10. The data classification device according to claim 9, wherein

the first learning unit deletes or disables the first learning data from the first storage unit when the learning processing of learning conversion processing of the conversion unit has been completed.

11. A data classification device comprising:

a conversion unit that converts input classification target data into a feature vector;

a classification unit that provides a label to the classification target data on the basis of the feature vector output by the conversion unit; and

a learning unit that learns conversion processing of the conversion unit, using accumulated data of the input classification target data, as learning data.

12. A data classification method comprising:

a conversion step of converting input classification target data into a feature vector;

a classification step of providing a label to the classification target data on the basis of the feature vector output by the conversion step;

a first learning step of learning conversion processing of the converting step, using accumulated data of the input classification target data, as first learning data; and

a second learning step of learning classification processing of the classification step, using second learning data in which a label has been provided to data of a same type as the classification target data.

13. A non-transitory computer readable storage medium having stored therein a control program causing a computer to execute a process, the process comprising:

a conversion unit that converts input classification target data into a feature vector;

a classification unit that provides a label to the classification target data on the basis of the feature vector output by the conversion unit; and

a first learning unit that learns conversion processing of the conversion unit, using accumulated data of the input classification target data, as first learning data; and

a second learning unit that learns classification processing of the classification unit, using second learning data in which a label has been provided to data of a same type as the classification target data.