Learning support device, learning support method, and learning support program

Abstract

To provide a learning support device, a method for supporting learning, and a learning support program which can acquire a quick and appropriate result of prediction using operational data even when a data drift is happening in the operational data.SOLUTION: The method includes the steps of: acquiring learning data and operational data (S101); extracting the feature amount of the learning data and the feature amount of the operational data from the learning data and the operational data (S102); calculating the result of prediction from the operational data on the basis of the feature amount of the operational data (S103); correcting the result of prediction on the basis of the feature amount of the learning data and the feature amount of the operational data (S104); and outputting the corrected result of prediction (S105).SELECTED DRAWING: Figure 9

Claims

1. An acquisition unit that acquires training data and operational data, A feature extraction unit extracts the features of the training data and the features of the operational data from the training data and the operational data, respectively. A prediction result calculation unit calculates a prediction result from the operational data based on the characteristics of the operational data, A correction unit corrects the prediction result based on the features of the training data and the features of the operational data. An output unit that outputs the corrected prediction result, A learning support device having the following features.

2. The learning support device according to claim 1, wherein the correction unit performs a correction that degrades the prediction result.

3. The system further includes a reliability calculation unit that calculates the reliability of the prediction result based on the features of the training data and the features of the operational data. The learning support device according to claim 2, wherein the correction unit performs a correction that degrades the prediction result based on the reliability.

4. The learning support device according to claim 3, wherein the confidence calculation unit calculates the confidence level based on the distance between the feature quantities of the learning data and the feature quantities of the operational data.

5. The prediction result of the operational data is the likelihood of the class with the highest likelihood among the predetermined classes. The learning support device according to claim 3, wherein the correction unit degrades the prediction result from the operational data by reducing the likelihood, which is the prediction result from the operational data, in accordance with the confidence level.

6. The acquisition unit acquires a plurality of the aforementioned training data as a training data group, The learning support device according to claim 3, wherein the confidence calculation unit calculates the confidence level based on the distance between the distribution of a plurality of features extracted from each training data in the training data group and the features of the operational data.

7. The learning support device according to claim 1, wherein the output unit further outputs the prediction result before correction.

8. The learning support device according to claim 1, wherein the prediction result is the prediction result predicted from the operational data by a neural network trained on the training data.

9. The aforementioned training data and operational data are image data or text data. The learning support device according to claim 1, wherein the prediction result calculation unit calculates the prediction result using a supervised machine learning model that has been trained with the combination of the learning data and the correct label corresponding to the learning data as training data.

10. Step (a) to acquire training data and operational data, (b) a step of extracting the features of the training data and the features of the operational data from the training data and the operational data, respectively, (c) A step of calculating a prediction result from the operational data based on the characteristics of the operational data, (d) A step of correcting the prediction result based on the features of the training data and the features of the operational data, Step (e) of outputting the corrected prediction result, A learning support method that has the following characteristics.

11. The learning support method according to claim 10, wherein in step (d), a correction is made to degrade the prediction result.

12. The method further includes a step (f) of calculating the reliability of the prediction result based on the features of the training data and the features of the operational data, The learning support method according to claim 11, wherein in step (d), a correction is made to degrade the prediction result based on the confidence level.

13. The learning support method according to claim 12, wherein in step (f), the confidence level is calculated based on the distance between the features of the learning data and the features of the operational data.

14. The prediction result of the operational data is the likelihood of the class with the highest likelihood among the predetermined classes. In step (d), the learning support method according to claim 12, wherein the likelihood, which is the prediction result from the operational data, is reduced in accordance with the confidence level, thereby degrading the prediction result from the operational data.

15. In step (a) above, multiple training data are acquired as a training data set, The learning support method according to claim 12, wherein in step (f), the confidence level is calculated based on the distance between the distribution of a plurality of features extracted from each training data in the training data group and the features of the operational data.

16. The learning support method according to claim 10, wherein in step (e), the prediction result before correction is further output.

17. The learning support method according to claim 10, wherein the prediction result is the prediction result predicted from the operational data by a neural network trained on the training data.

18. The aforementioned training data and operational data are image data or text data. The learning support method according to claim 10, wherein in step (c), the prediction result is calculated using a supervised machine learning model that has been trained with the combination of the learning data and the correct label corresponding to the learning data as training data.

19. A learning support program for causing a computer to execute the learning support method described in any one of claims 10 to 18.

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