Autoencoder Adaptability Calculation for ML Model Selection
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Solution Overview
Problem
Existing machine learning technologies face challenges in determining the adaptability of test data to learning models, leading to incorrect model selection and classification failures during sequential learning, especially when dealing with multiple learning models generated from different data sets.
Innovation Solution
The implementation of an autoencoder-based adaptability calculation method, where input data is processed through a learning model and an encoder, and the output is fed into a decoder to calculate the adaptability of the learning model to the input data, reducing the likelihood of selecting a wrong learning model by using reconstruction error as an indicator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple learning models are used for sequential learning, then the classification coverage is improved, but the reliability of model selection deteriorates due to inability to determine adaptability
Solution Approach 1:
An autoencoder is introduced as an intermediary component between the learning model and the test data. The autoencoder processes the test data through encoding and decoding to generate a reconstruction, and the difference between the original and reconstructed data is calculated as adaptability. This intermediary mechanism enables reliable assessment of whether a learning model is suitable for specific test data without directly comparing models.
Solution Approach 2:
The patent replaces traditional mechanical model selection methods with an information-theoretic approach using autoencoding. Instead of relying on heuristic or manual model selection, the system uses the autoencoder to quantitatively measure the information loss when compressing test data, providing an objective criterion for model adaptability assessment.
2Device complexity
If conventional model selection methods are used, then the device complexity is reduced, but the measurement precision of model adaptability deteriorates
Solution Approach 1:
The autoencoder is trained in advance on the learning data before actual model selection occurs. This preliminary training allows the system to establish a baseline for what constitutes appropriate data representation, enabling precise measurement of model adaptability during the selection phase without adding complex real-time computation.
Solution Approach 2:
The patent creates a simplified representation (encoding) of the test data through the autoencoder, which captures the essential features while discarding redundant information. This copying process enables precise adaptability measurement by comparing the fidelity of the copied representation against the original data, providing a computationally efficient approach.
Data Source
AI summary
An adaptability calculation device inputs input data to a learning model and an encoder of an autoencoder that have performed learning with learning data, inputs an output from the learning model and an output from the encoder of the autoencoder to a decoder of the autoencoder, and calculates adaptability of the output from the learning model to the input data based on an output from the decoder and the input data.