Temporal Sequence Classification for Training Data Error Detection
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Solution Overview
Problem
The development of machine-learning models requires large training datasets to ensure robustness and avoid overfitting, particularly in environments where data quality can vary, leading to inaccuracies in predictions.
Innovation Solution
A method that processes temporal sequences of data portions to identify temporal patterns, flagging and correcting erroneous data points by comparing them to established patterns, thereby enhancing the quality of training datasets for machine-learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large training dataset is used to ensure robustness and avoid overfitting, then the model's reliability improves, but the impact of erroneous data portions increases
Solution Approach 1:
The patent applies preliminary action by identifying and correcting erroneous data portions in the training dataset before the machine learning model training process begins. The system processes temporal sequences of data portions, identifies deviations from expected patterns, and corrects these errors in advance, ensuring that only high-quality data is used for training. This preliminary correction prevents erroneous data from negatively impacting model robustness while maintaining the benefits of using large datasets.
2Measurement precision
If manual verification of each data portion is performed to ensure data quality, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent implements self-service by enabling the data processing system to automatically identify and correct erroneous data portions without requiring manual verification. The system uses temporal pattern recognition and deviation detection algorithms to autonomously quality-check training data, eliminating the need for human reviewers while maintaining high data accuracy. This automated self-verification process preserves measurement precision while avoiding the productivity loss associated with manual data checking.
3Manufacturing precision
If temporal pattern processing is applied to identify erroneous data portions, then data quality improves, but device complexity increases
Solution Approach 1:
The patent uses an intermediary approach by introducing a dedicated data quality processing system that acts as a mediator between raw temporal data sequences and the machine learning training process. This intermediary system specializes in temporal pattern recognition and error detection, handling the complexity of temporal analysis separately from the main model training workflow. By isolating the complex temporal processing in a dedicated intermediary component, the overall system complexity is managed while still achieving improved data quality through temporal pattern analysis.
Data Source
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AI summary
A mechanism for identifying potentially erroneous first data portions within a time-ordered sequence of first data portions. Each first data portion indicates a classification of a corresponding second data portion of a time-ordered sequence of second data portions. The time-ordered sequence of first data portions is processed to identify temporal pattern information, which indicates a temporal pattern of how the classifications change over time. The temporal pattern information and time-ordered sequence of first data portions are processed to identify any first data portion that deviates from the temporal pattern.