Information Classification Maps for User-Guided Group Reclassification
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Solution Overview
Problem
Existing text data classification systems struggle to accurately reflect user-intended relationships between classified data, making it difficult to assign appropriate feature elements based on overall data tendencies.
Innovation Solution
An information processing system that includes an acquisition, classification, reception, and update processing unit, allowing users to interact with a classification map to integrate or reclassify data groups, thereby updating the classification results to align with their intended features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic classification based on frequently appearing words is used, then classification efficiency is improved, but the ability to reflect user-intended relationships deteriorates
Solution Approach 1:
The system displays extracted feature elements to users and receives feedback through selection operations. Users can select which feature elements to keep or modify, and the system re-performs classification based on this feedback, creating an iterative improvement loop that refines classification accuracy while maintaining efficiency.
Solution Approach 2:
The system performs preliminary classification using automatic word frequency analysis to generate initial groups and extract feature elements before user review. This preliminary action provides a starting point that maintains classification efficiency while setting the stage for subsequent user refinement to improve accuracy.
2Measurement precision
If manual feature element assignment is used, then accuracy of feature representation is improved, but operation complexity increases
Solution Approach 1:
The system automatically extracts feature elements from classified data groups and presents them to users for selection. This self-service approach to feature extraction reduces the complexity of manual feature assignment while maintaining accurate feature representation through user validation.
Solution Approach 2:
The system acts as an intermediary by automatically generating feature element candidates from classification results and presenting them to users. This intermediary role simplifies the user's task from creating features from scratch to simply selecting or modifying pre-extracted features, reducing operation complexity while maintaining accuracy.
3Quantity of substance
If comprehensive data classification is performed, then completeness of classification is improved, but understanding of overall data tendency deteriorates
Solution Approach 1:
The system extracts representative feature elements from each classified data group and displays them prominently in the classification map. This extraction approach allows users to understand the overall data tendency through selected feature elements while maintaining complete classification of all data points, preventing information loss about data characteristics.
Data Source
AI summary
An information processing system includes: an acquisition processing unit that acquires a plurality of pieces of data to be classified; a classification processing unit that classifies the plurality of pieces of data acquired by the acquisition processing circuit into a plurality of groups, extracts a feature element representing a feature of a group for each of the plurality of classified groups, and generates a classification map in which the feature element is displayed in association with the group; a reception processing unit that receives an operation of selecting the predetermined feature element in the classification map from a user; and an update processing unit that executes processing of changing the group based on the feature element selected by the user and updates the classification map.