Sequential Data Classification Using Integrated Element Indicators
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Solution Overview
Problem
Existing information processing techniques using Sequential Probability Ratio Test (SPRT) assume independent and identically distributed 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 data elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If SPRT is used to classify sequential data assuming independent and identically distributed elements, then the calculation process is simple, but the classification accuracy is insufficient because the relationship between data elements is not considered
Solution Approach 1:
The patent segments the sequential data classification process into multiple stages: (1) dividing data elements into groups, (2) calculating indicators for each group considering relationships between elements, (3) integrating group indicators to form an overall classification indicator. This segmentation allows the system to maintain calculation simplicity while improving accuracy by considering element relationships within groups without requiring complex analysis of all elements simultaneously.
Solution Approach 2:
The patent merges multiple data elements into groups and combines their individual indicators through integration to form a comprehensive classification indicator. By merging elements into groups and integrating their indicators, the system captures relationships between elements while maintaining a manageable calculation structure, thus improving classification accuracy without excessive complexity.
2Measurement precision
If the relationship between all elements of sequential data is considered to improve classification accuracy, then the classification accuracy improves, but the calculation complexity increases significantly
Solution Approach 1:
The patent reduces calculation complexity by segmenting the set of all data elements into smaller groups. Instead of analyzing relationships among all elements simultaneously, the system analyzes relationships within each group separately, then integrates the results. This segmentation maintains classification accuracy by preserving element relationships while making the calculation process manageable and scalable.
Solution Approach 2:
The patent applies partial action by considering relationships within subsets of elements (groups) rather than requiring analysis of all possible element relationships. This partial consideration of relationships achieves sufficient classification accuracy for practical applications while avoiding the combinatorial explosion of complexity that would result from analyzing all element relationships comprehensively.
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.