Empirical Model for Classifier Performance Estimation
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Solution Overview
Problem
Current classifier systems lack an effective method to estimate performance when presented with unknown data, as they rely on confusion matrices generated from known data and fail to predict performance accurately for out-of-library data.
Innovation Solution
A computer-implemented method and system that utilize an empirical model based on two density functions to capture correct and incorrect performance of in-library categories, along with an additional density function for out-of-library probability, allowing prediction of performance with out-of-library data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a confusion matrix is generated from known data to characterize classifier system performance, then the performance measurement is accurate for in-library data, but the prediction capability for out-of-library data is insufficient
Solution Approach 1:
The patent applies preliminary action by generating an empirical model in advance from the observed confusion matrix using density functions. This model captures the classifier's performance characteristics before encountering out-of-library data, enabling predictions to be made without requiring actual out-of-library test results. The empirical model serves as a pre-computed representation that can be applied to predict performance on unknown data categories.
2Reliability
If traditional confusion matrix evaluation is used, then performance on known categories is well-characterized, but estimation for unknown categories becomes unreliable
Solution Approach 1:
The patent applies parameter changes by transforming the discrete confusion matrix data into continuous probability density functions. By fitting density functions to the confusion matrix entries and using statistical parameters (mean, variance) to characterize performance, the system can generalize from known categories to unknown categories through parameter-based predictions rather than direct matrix lookup.
Data Source
AI summary
A method and system of estimating the performance of a classifier system based on a reported confusion matrix includes, in one embodiment, parameters fit to observed confusion matrices, such that the expected performance of decision detection versus the probability of not-in-library reports can be estimated based on the forced decision confusion matrix. The approach also lends itself to a general methodology for modeling classes of confusers in a statistical manner, which can be extended to modeling clutter severity.