Classification Count Adjustment System for Category Size Estimation
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Solution Overview
Problem
Automated classifiers often make mistakes that introduce bias in estimating the frequency of items in a category, as false positives and false negatives do not cancel each other out, skewing the observed frequency and affecting the accuracy of category size estimates.
Innovation Solution
A classification count adjustment system that computes an adjusted estimate based on the count estimate provided by a classifier and measures of its behavior, such as true positive and false positive rates, to correct for classification errors and improve the accuracy of category size estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classifiers are used to count items in categories, then classification speed and automation are improved, but measurement precision deteriorates due to classifier mistakes and bias
Solution Approach 1:
The system uses a calibration dataset with known ground truth labels to measure classifier performance metrics (precision, recall, true positive rate, false positive rate). These measured metrics are fed back into the adjustment calculation to correct the raw classifier counts, transforming the open-loop classification system into a closed-loop system that compensates for its own errors.
Solution Approach 2:
The system changes the parameters used for counting by not relying on raw classifier output counts, but instead using adjusted counts that incorporate classifier performance parameters (precision, recall, TPR, FPR). The adjustment formula transforms the parameter space from raw counts to corrected estimates that account for classifier behavior characteristics.
2Device complexity
If classifier counts are used directly, then device complexity is reduced, but measurement precision deteriorates due to uncorrected classification errors
Solution Approach 1:
The system performs preliminary calibration by measuring classifier performance on a calibration dataset before using the classifier for actual counting tasks. This preliminary action captures the classifier's error characteristics in advance, allowing subsequent counts to be adjusted without adding complexity to the main classification operation.
Solution Approach 2:
The system introduces an intermediary adjustment calculation that acts as a mediator between the raw classifier output and the final category size estimate. This intermediary layer applies correction factors based on classifier metrics, decoupling the complexity of error correction from both the classification process and the final reporting.
3Measurement precision
If more classifier metrics are collected and processed, then measurement precision improves through better adjustment, but device complexity increases
Solution Approach 1:
The system extracts only the essential classifier performance metrics needed for adjustment (precision, recall, or equivalently TPR and FPR) from the full range of possible classifier outputs. By focusing on these specific extracted metrics rather than all possible performance measures, the system achieves correction with minimal processing complexity.
Data Source
AI summary
A classification count adjustment system for adjusting a count estimate of items in a dataset D classified into a class is disclosed. The system includes a count estimate produced by a classifier of the number of items in the dataset D classified into the class. The system further comprises one or more measures of behavior of the classifier, indicating the ability of the classifier to classify items into the class. The system further comprises a processor for computing an adjusted estimate based on the count estimate by the classifier and the one or more measures of behavior.