Outperformance Score: A Universal Standardization Method for Confusion-Matrix-Based Classification Performance Metrics
- URL: http://arxiv.org/abs/2505.07033v1
- Date: Sun, 11 May 2025 16:07:14 GMT
- Title: Outperformance Score: A Universal Standardization Method for Confusion-Matrix-Based Classification Performance Metrics
- Authors: Ningsheng Zhao, Trang Bui, Jia Yuan Yu, Krzysztof Dzieciolowski,
- Abstract summary: We introduce the outperformance score function, a universal standardization method for confusion-matrix-based classification performance metrics.<n>The outperformance score represents the percentile rank of the observed classification performance within a reference distribution of possible performances.
- Score: 1.5186937600119894
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Many classification performance metrics exist, each suited to a specific application. However, these metrics often differ in scale and can exhibit varying sensitivity to class imbalance rates in the test set. As a result, it is difficult to use the nominal values of these metrics to interpret and evaluate classification performances, especially when imbalance rates vary. To address this problem, we introduce the outperformance score function, a universal standardization method for confusion-matrix-based classification performance (CMBCP) metrics. It maps any given metric to a common scale of $[0,1]$, while providing a clear and consistent interpretation. Specifically, the outperformance score represents the percentile rank of the observed classification performance within a reference distribution of possible performances. This unified framework enables meaningful comparison and monitoring of classification performance across test sets with differing imbalance rates. We illustrate how the outperformance scores can be applied to a variety of commonly used classification performance metrics and demonstrate the robustness of our method through experiments on real-world datasets spanning multiple classification applications.
Related papers
- Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions [8.640930010669042]
We propose a unimodal regularisation approach to improve the classification performance of the first and last classes.
Performance in the extreme classes is compared using a new metric that takes into account their sensitivities.
The results for the proposed metric show that the generalised beta distribution generally improves classification performance in the extreme classes.
arXiv Detail & Related papers (2024-07-17T08:57:42Z) - Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment [54.179859639868646]
We propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking.
xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics.
We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories.
arXiv Detail & Related papers (2023-07-27T07:42:44Z) - Class-Conditional Conformal Prediction with Many Classes [60.8189977620604]
We propose a method called clustered conformal prediction that clusters together classes having "similar" conformal scores.
We find that clustered conformal typically outperforms existing methods in terms of class-conditional coverage and set size metrics.
arXiv Detail & Related papers (2023-06-15T17:59:02Z) - Parametric Classification for Generalized Category Discovery: A Baseline
Study [70.73212959385387]
Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples.
We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem.
We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
arXiv Detail & Related papers (2022-11-21T18:47:11Z) - When in Doubt: Improving Classification Performance with Alternating
Normalization [57.39356691967766]
We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification.
CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution.
We empirically demonstrate its effectiveness across a diverse set of classification tasks.
arXiv Detail & Related papers (2021-09-28T02:55:42Z) - Appropriateness of Performance Indices for Imbalanced Data
Classification: An Analysis [15.85259386116784]
We identify two fundamental conditions that a performance index must satisfy to be respectively resilient to altering number of testing instances from each class and the number of classes in the test set.
We investigate the capability of the indices to retain information about the classification performance over all the classes, even when the classifier exhibits extreme performance on some classes.
arXiv Detail & Related papers (2020-08-26T18:23:36Z) - Classification Performance Metric for Imbalance Data Based on Recall and
Selectivity Normalized in Class Labels [0.0]
We introduce a new performance measure based on the harmonic mean of Recall and Selectivity normalized in class labels.
This paper shows that the proposed performance measure has the right properties for the imbalanced dataset.
arXiv Detail & Related papers (2020-06-23T20:38:48Z) - Classifier uncertainty: evidence, potential impact, and probabilistic
treatment [0.0]
We present an approach to quantify the uncertainty of classification performance metrics based on a probability model of the confusion matrix.
We show that uncertainties can be surprisingly large and limit performance evaluation.
arXiv Detail & Related papers (2020-06-19T12:49:19Z) - Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking
Fairness and Algorithm Utility [54.179859639868646]
Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data.
There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups.
We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
arXiv Detail & Related papers (2020-06-15T10:08:39Z) - An Effectiveness Metric for Ordinal Classification: Formal Properties
and Experimental Results [9.602361044877426]
We propose a new metric for Ordinal Classification, Closeness Evaluation Measure, rooted on Measurement Theory and Information Theory.
Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously.
arXiv Detail & Related papers (2020-06-01T20:35:46Z) - Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
Machine learning algorithms are susceptible to data poisoning attacks.
We present a unifying view of randomized smoothing over arbitrary functions.
We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
arXiv Detail & Related papers (2020-02-07T21:28:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.