Similarity-Based Ranked Confusion Matrix for Fuzzy Attribute Evaluation
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Solution Overview
Problem
Existing face attribute recognition systems struggle to accurately evaluate the classification effect of fuzzy attributes, as they rely on traditional indicators that fail to reflect the similarity between categories, leading to misalignment between evaluation results and actual classification performance, especially for attributes with blurred boundaries.
Innovation Solution
A method and device that generate and display a similarity-based ranked confusion matrix, ranking categories based on similarities and updating matrix elements with true and predicted values to represent the number of samples, providing a more accurate representation of classification accuracy by distributing non-zero elements closer to the main diagonal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification indicators (such as single-label precision rate) are used to evaluate fuzzy attribute classification, then the evaluation process is simple, but the evaluation results do not reflect the actual classification performance and similarity between categories
Solution Approach 1:
The patent segments the evaluation process by dividing categories into different similarity levels (high similarity, medium similarity, low similarity) and evaluating classification performance separately for each level. This allows the system to capture nuanced classification accuracy while maintaining a structured, manageable evaluation framework that doesn't require complete reevaluation of all categories simultaneously.
Solution Approach 2:
The patent introduces a new evaluation dimension by creating a confusion matrix that incorporates category similarity information. Instead of using a single precision rate scalar value, the system generates a multi-dimensional evaluation structure that includes similarity-based category relationships, enabling more comprehensive assessment of classification performance for fuzzy attributes.
2Measurement precision
If categories are ranked based on similarity for fuzzy attributes, then the classification effect is more accurately reflected, but the evaluation method becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-ranking categories based on their similarity relationships before performing classification evaluation. The confusion matrix is constructed with categories ordered by similarity, so that when classification results are evaluated, the inherent similarity structure is already accounted for. This preprocessing step enables more accurate evaluation without requiring complex real-time calculations during the assessment phase.
3Measurement precision
If a similarity-based ranked confusion matrix is generated to show distribution of non-zero elements, then classification accuracy is better represented, but the computational process requires more steps
Solution Approach 1:
The system performs self-service by automatically generating the similarity-based ranked confusion matrix through programmatic processing of classification results. The evaluation methodology itself is implemented as an automated algorithm that takes classification outputs and systematically constructs the confusion matrix with similarity-based category ranking, eliminating the need for manual evaluation while maintaining high precision in accuracy representation.
Data Source
AI summary
A method is provided for evaluating an effect of classifying a fuzzy attribute of an object, the fuzzy attribute referring to an attribute, a boundary between two similar ones of a plurality of categories of which is blurred, wherein the method includes: generating a similarity-based ranked confusion matrix, which comprises: based on similarities of K categories of the fuzzy attribute of the object, ranking the K categories, where K is an integer greater than or equal to 2, generating a K×K all-zero initialization matrix, wherein an abscissa and an ordinate of the initialization matrix respectively represent predicted values and true values of the similarity-based ranked categories of the fuzzy attribute, and based on the true values and the predicted values of the category of the fuzzy attribute for the multiple object samples, updating values of corresponding elements in the initialization matrix; and displaying the similarity-based ranked confusion matrix.