Information Processing for Correlation-Aware Sequential Data Classification
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Solution Overview
Problem
Existing sequential probability ratio test (SPRT) methods assume independent and identically distributed elements, failing to consider the relationship between elements in sequential data, leading to insufficient accuracy in classification.
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
1Productivity
If SPRT is used to classify sequential data, then the classification process is simple and fast, but classification accuracy deteriorates when elements have strong correlations
Solution Approach 1:
The patent segments the classification process into two stages: first calculating indicators for each element independently, then integrating these indicators to determine the final classification. This segmentation allows the system to maintain computational efficiency while incorporating relational information between elements through the integration step.
Solution Approach 2:
The patent introduces a new dimension of analysis by calculating indicators that consider not only individual elements but also relationships between multiple elements. This extends the traditional SPRT approach from unidimensional (single element) to multidimensional (multiple elements with relationships) analysis, improving accuracy without significantly increasing computational complexity.
2Device complexity
If traditional SPRT is used, then computational complexity is low, but it fails to consider relationships between data elements
Solution Approach 1:
The patent performs preliminary calculation of indicators for each element before integrating them. By pre-calculating these indicators while considering relationships between elements, the system prepares structured information that can be efficiently integrated, maintaining reasonable computational complexity while improving reliability.
Solution Approach 2:
The patent introduces indicators as intermediary objects that bridge individual element analysis and overall classification. These indicators encapsulate both individual element characteristics and relational information, serving as mediators that integrate multiple dimensions of information without directly implementing complex relational computations.
3Adaptability or versatility
If SPRT assumes independent and identically distributed elements, then the mathematical model is simple, but classification accuracy deteriorates for correlated data
Solution Approach 1:
The patent changes the parameters of the classification model by introducing indicators that incorporate relational information between elements. Instead of using fixed independence assumptions, the system dynamically adjusts the analysis to consider relationships between elements, improving accuracy for correlated data while maintaining model simplicity through the indicator integration approach.
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.