Classifier Performance Prediction via Ensemble Divergence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing classifiers face challenges in accurately predicting their performance on new, out-of-distribution data due to domain shifts, which can occur due to changes in environmental conditions or unseen data distributions.
Innovation Solution
A method is proposed that uses an ensemble of classifiers trained on the same distribution to predict the performance of a given classifier by calculating pairwise divergences between classification scores. This approach considers the full predictive label distribution rather than just a single class decision, providing a more sensitive measure of agreement between classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier is trained on a large dataset of training examples, then the classification accuracy on training data is improved, but the accuracy on unseen out-of-distribution data deteriorates due to domain shift
Solution Approach 1:
The method performs preliminary evaluation by computing classification scores from the given sample using the trained classifier, then calculates divergences between these scores and reference scores from training examples. This preliminary action allows prediction of performance on out-of-distribution data without actually training on the new data, thus maintaining training accuracy while predicting reliability on unseen data.
Solution Approach 2:
The method introduces an intermediary measure - the divergence between classification scores - to bridge the gap between training data performance and out-of-distribution data performance. By comparing the divergence of the given sample's classification scores against reference scores from training examples, the system can predict future performance without direct retraining, thus resolving the contradiction between training accuracy and out-of-distribution reliability.
2Loss of information
If the classifier outputs classification scores for multiple classes, then the information content is improved, but the complexity of evaluating performance deteriorates
Solution Approach 1:
The method extracts the divergence metric from the full classification score vectors, focusing only on the disagreement between the given sample's scores and reference scores. By taking out just the divergence component rather than analyzing all classification scores individually, the system maintains information content while simplifying the evaluation complexity to a single comparable value.
Solution Approach 2:
The method segments the performance evaluation into two independent parts: computing classification scores for the given sample (which preserves full information content) and computing the divergence between these scores and reference scores (which simplifies evaluation). This segmentation allows the system to maintain rich classification information while reducing evaluation complexity to a straightforward divergence calculation.
Data Source
AI summary
A method for predicting the performance of a given classifier with respect to one or more given samples of input data. The method includes: providing further classifiers that, together with the given classifier f*, form a set F of classifiers f; computing, for the x, using each classifier f from the set F, classification scores fk(x) with respect to all available classes k=1, . . . , K covered by the classifiers; determining, for pairs (f,f′) of classifiers f and f′ from the set F, divergences of the classification scores fk(x) and f′k(x) for all k=1, . . . , K as pairwise divergences dis(f,f′,x) of the classifiers f and f′ with respect to the one or more samples x; and determining the performance P(f*,x) of the classifier f* with respect to the one or more samples x based at least in part on pairwise divergences dis(f*,f′,x) between the classifier f* and other classifiers f′.