Classifier Reliability via Output Variance
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Solution Overview
Problem
Existing image classification systems face difficulties in accurately calculating the reliability of classification results due to variations in output accuracy from different classifiers used in combination, which affects the overall accuracy of age estimation and other attributes in images.
Innovation Solution
An identification apparatus comprising a data obtaining unit, feature quantity obtaining unit, multiple classifiers, identification unit, and reliability generation unit that generates a single classification result and reliability based on variations across multiple classification results, with the reliability unit using variance or standard deviation to quantify accuracy and an evaluation unit to weight classifier outputs based on accuracy evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple classifiers with different accuracy levels are combined, then classification accuracy is improved through ensemble learning, but calculating the reliability of the entire apparatus becomes difficult
Solution Approach 1:
The patent transforms the reliability calculation problem into a statistical parameter calculation problem by computing the variance of classification results across multiple classifiers. This parameter change enables quantitative reliability assessment without requiring complex probabilistic models, resolving the contradiction between improved accuracy through ensemble learning and the difficulty of reliability calculation.
Solution Approach 2:
The patent replaces complex reliability modeling with a straightforward statistical calculation approach. Instead of using complex probabilistic frameworks to assess reliability, the system uses variance computation on classification results, substituting a mechanically simple statistical operation for a complex reliability assessment problem.
2Adaptability or versatility
If different classifiers are used to improve classification accuracy, then the system can handle diverse classification tasks, but the variations in output accuracy make it difficult to quantify overall reliability
Solution Approach 1:
The patent segments the reliability assessment into individual classifier performance evaluation and then aggregates the results through variance calculation. By evaluating each classifier's output and computing the variance across these outputs, the system can quantify overall reliability while maintaining the ability to handle diverse classification tasks through ensemble learning.
3Ease of operation
If a single classification result is generated from multiple classifiers, then the system provides a unified output, but the variations across classifier results make reliability assessment challenging
Solution Approach 1:
The patent introduces feedback by calculating the variance of classification results and using this information to assess reliability. The system feeds back the variation information from individual classifier outputs to the overall reliability assessment, enabling the unified output to be evaluated for reliability based on the consistency of underlying classifier results.
Data Source
AI summary
An identification apparatus performs classification using a plurality of classifiers, and calculates the reliability of its classification result. A data obtaining unit obtains input data. A feature quantity obtaining unit obtains a feature quantity corresponding to the input data. A plurality of classifiers receive input of the feature quantity and perform classification based on the input feature quantity. An identification unit inputs the feature quantity into each of the classifiers, and generates a single second classification result based on a plurality of classification results obtained from the classifiers. A reliability generation unit generates a reliability of the second classification result based on variations across the plurality of classification results.