Feature Relationship Visualization Using Importance-Based Extraction
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Solution Overview
Problem
Existing data visualization techniques struggle to automatically identify and highlight useful features without user intervention, particularly for users lacking domain knowledge or data analysis expertise.
Innovation Solution
A visualization device that includes an analysis unit to assess feature importance, an extraction unit to select feature combinations, and a drawing unit to prioritize and visualize these features based on their importance and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated feature importance analysis is implemented, then visualization accuracy of useful features is improved, but device complexity increases
Solution Approach 1:
The system segments the complex visualization task into distinct functional modules: an analysis unit that calculates feature importance scores, an extraction unit that selects important feature combinations, and a drawing unit that visualizes them. This segmentation allows each module to specialize in one aspect, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw data and visualization output. The analysis unit computes importance scores as intermediate representations, and the extraction unit further processes these scores to identify meaningful feature combinations. This intermediary layer acts as a mediator that transforms complex data relationships into可视化-friendly formats.
2Ease of operation
If automated feature extraction is implemented, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The system changes the parameter representation from raw feature values to importance scores. The analysis unit transforms complex multi-dimensional feature relationships into scalar importance scores that preserve the essential information while making the data more manageable for automated processing and visualization.
Solution Approach 2:
Instead of extracting all possible feature combinations, the system selectively extracts only those feature combinations whose importance scores meet predetermined thresholds. This partial action approach focuses computational resources on the most significant features, maintaining ease of operation while minimizing information loss by preserving the most relevant data aspects.
3Manufacturing precision
If comprehensive feature analysis is performed, then visualization quality is improved, but processing time increases
Solution Approach 1:
The analysis unit performs preliminary analysis by calculating importance scores for all features before the actual visualization process. This preliminary action identifies and prioritizes important features in advance, allowing the drawing unit to focus computational resources on visualizing only the most significant feature combinations, thereby improving visualization quality while reducing overall processing time.
Solution Approach 2:
The system dynamically adjusts the depth and scope of feature analysis based on the calculated importance scores. High-scoring features receive more detailed analysis and visualization attention, while lower-scoring features are processed more efficiently or omitted. This dynamic approach optimizes the balance between comprehensive analysis and processing efficiency.
Data Source
AI summary
In accordance with a score representing a relationship between features of input data, an analysis unit analyzes the importance of each feature. An extraction unit extracts a combination of features in which the score satisfies a predetermined condition. A specification unit specifies a visualization system for the combination in accordance with at least the types and connection relationships of the features constituting the extracted combination. A drawing unit draws the relationship between the features constituting the combination in order of priority according to the importance, based on the combination of the features and the visualization system corresponding to the combination.