Processing Execution System Using Validity Checks for Limited Training Data
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Solution Overview
Problem
Existing models for data classification struggle to provide accurate results when training data is insufficient, leading to inappropriate outputs and a lack of useful information for users.
Innovation Solution
A processing execution system that includes a second classification information acquisition module, a validity estimation module, and an execution module to enhance model accuracy by using a second model to validate and refine the outputs of a first model based on a predetermined estimation method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is used for data classification, then classification capability is provided, but when training data is insufficient, the model accuracy becomes insufficient leading to inappropriate outputs
Solution Approach 1:
A validity estimation module is introduced as an intermediary between the first classification model and the final output. This module estimates the validity of each classification result using a predetermined estimation method (such as confidence scores, entropy measures, or cross-validation techniques), allowing the system to identify and handle low-confidence predictions without requiring extensive additional training data.
Solution Approach 2:
The system changes the parameter of validity estimation by applying different estimation methods or thresholds to classification results. When the estimated validity falls below a certain threshold, the system triggers alternative processing such as requesting additional information, using a different classification approach, or flagging for manual review, thereby maintaining reliability without proportionally increasing training data volume.
2Measurement precision
If more training data is generated to improve model accuracy, then classification precision improves, but the time and resources required for data generation and model training increase
Solution Approach 1:
The validity estimation is performed preliminarily on each classification result before final output. By estimating validity using computationally efficient methods (such as confidence score calculation or entropy measurement) rather than retraining the model, the system achieves high classification precision without the time cost of extensive data generation and model retraining.
Solution Approach 2:
Instead of creating new training data copies to improve model accuracy, the system uses the existing model outputs and applies validity estimation techniques that replicate the assessment process efficiently. This allows multiple validity checks on the same classification results without duplicating the expensive model training process.
3Productivity
If the model outputs classification results with insufficient training data, then processing speed is maintained, but the quality of search results and user satisfaction decrease
Solution Approach 1:
The validity estimation module provides immediate feedback on the quality of each classification result. When validity is low, the system can trigger feedback loops such as presenting alternative classifications, requesting user feedback, or switching to a different processing path, thereby improving search result quality without significantly impacting processing speed through efficient feedback mechanisms.
Solution Approach 2:
The system dynamically adjusts its output behavior based on the estimated validity of classification results. High-validity results are output directly to maintain processing speed, while low-validity results trigger additional processing steps such as alternative model invocation, manual review routing, or enhanced verification, thereby adapting search result quality to the confidence level without uniformly slowing down all processing.
Data Source
AI summary
A processing execution system, comprising at least one processor configured to: acquire second classification information relating to a classification of second data based on a first model which has learned a relationship between first data and first classification information relating to a classification of the first data; estimate, based on a predetermined estimation method, a validity relating to a combination of the second data and the second classification information; and execute predetermined processing based on an estimation result for the validity.