Sequential Data Classification With Relationship-Aware Indicator Integration
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Solution Overview
Problem
Existing sequential probability ratio test (SPRT) methods assume independent and identically distributed elements in sequential data, leading to insufficient accuracy due to neglecting the relationship between data elements.
Innovation Solution
An information processing device and method that calculates indicators for each element considering multiple elements, integrates these indicators to determine an integrated indicator, and classifies sequential data based on this integrated indicator, accounting for the relationships between elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SPRT is used to classify sequential data, then the classification process is simple and fast, but the classification accuracy is insufficient because the method does not consider the relationship between data elements
Solution Approach 1:
The patent segments the classification task into two stages: first calculating indicators for each individual element, then integrating these indicators to classify the sequential data. This segmentation allows the system to handle complex relationships between elements while maintaining computational efficiency through modular processing.
Solution Approach 2:
The patent introduces a new dimension of analysis by considering not only individual elements but also the relationships between multiple elements simultaneously. This dimensional expansion from single-element analysis to multi-element interaction enables the system to capture temporal dependencies and improve classification accuracy without completely redesigning the entire classification framework.
2Reliability
If the SPRT method is applied, then the computational load is low, but the method fails to account for correlations between sequential data elements
Solution Approach 1:
The patent performs preliminary calculations of indicators for each element before the final classification decision. By pre-computing these indicators and storing them, the system can make reliable classification decisions without performing expensive real-time calculations during the actual classification process, thus reducing overall computational energy consumption while improving reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the indicators calculated for each element are integrated to form a comprehensive assessment of the sequential data. This feedback loop allows the system to continuously refine its classification based on the relationships between elements, improving reliability without requiring excessive computational resources.
3Productivity
If individual element analysis is performed, then the processing speed is high, but the classification accuracy decreases when data elements are correlated
Solution Approach 1:
The patent merges the results of individual element analyses by integrating their indicators into a unified classification decision. This combining process allows the system to maintain the speed benefits of individual element processing while achieving the accuracy benefits of considering element relationships, as the integration step efficiently aggregates information from multiple elements.
Solution Approach 2:
The patent adds a temporal dimension to the analysis by considering the sequence order and relationships between elements. This dimensional addition enables the system to capture correlations and temporal dependencies that individual element analysis misses, improving accuracy without significantly reducing processing speed through efficient integration methods.
Data Source
AI summary
Provided is an information processing device including: an acquisition unit that sequentially acquires a plurality of elements included in sequential data; a first calculation unit that calculates, for each of the plurality of elements, indicators each indicating which of a plurality of classes is appropriate for corresponding element to belong to, in consideration of two or more elements among the plurality of elements; a second calculation unit that calculates, by integrating the indicators of the plurality of elements, an integrated indicator indicating which of the plurality of classes is appropriate for the sequential data to belong to; and a classification unit that classifies the sequential data into one of the plurality of classes based on the integrated indicator.