Learning Data Selection for Estimation Model Accuracy
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Solution Overview
Problem
Existing machine learning models struggle to accurately estimate the factors contributing to classification or determination in input data, especially when the data is transformed for processing, leading to reduced determination accuracy.
Innovation Solution
A learning data selection method that calculates distances between input data and transformed data to select appropriate learning target data, ensuring that the estimation model learns from relevant data points in both feature spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIME method is used to estimate classification factors in transformed data space, then estimation can be performed on transformed data, but the estimation does not correspond to factors in original input data space
Solution Approach 1:
The patent introduces an intermediary mapping process that connects the transformed data space back to the input data space. By calculating distances in both spaces and using the transformation relationship, the system mediates between the two feature spaces to identify which input data factors correspond to the transformed data factors, thus preserving the interpretability of original input data while utilizing transformed data for estimation.
2Reliability
If data is transformed into dimensions for classifier processing, then classification accuracy is improved, but the relationship between classification factors and original input data becomes unclear
Solution Approach 1:
The patent implements a feedback mechanism by calculating distances between input data and transformed data, then using this distance information to identify corresponding factors. The system feeds back the transformation relationship to map classification factors from transformed space back to input data space, making the factors detectable and measurable in terms of original input data while maintaining high classification accuracy.
Data Source
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AI summary
To perform selection of learning data of an estimation model that estimates an output factor of a machine learning model that performs classification and determination with respect to transformed data that is transformed from input data, 1) a first input data group relating to first input data is extracted (S12) in correspondence with designation of the first input data included in an input data group input to the machine learning model, 2) a first transformed data group that is input (S14) to the machine learning model and corresponds to the first input data group, and a first output data group of the machine learning model which corresponds to the first transformed data group are respectively acquired, and 3) learning target data of the estimation model is selected (S16) from the first input data group.