The Tile: A 2D Map of Ranking Scores for Two-Class Classification
- URL: http://arxiv.org/abs/2412.04309v2
- Date: Wed, 18 Dec 2024 12:50:29 GMT
- Title: The Tile: A 2D Map of Ranking Scores for Two-Class Classification
- Authors: Sébastien Piérard, Anaïs Halin, Anthony Cioppa, Adrien Deliège, Marc Van Droogenbroeck,
- Abstract summary: We present a novel versatile tool, named the Tile, that organizes an infinity of ranking scores in a single 2D map for two-class classifiers.
We study the properties of the underlying ranking scores, such as the influence of the priors or the correspondences with the ROC space.
- Score: 10.89980029564174
- License:
- Abstract: In the computer vision and machine learning communities, as well as in many other research domains, rigorous evaluation of any new method, including classifiers, is essential. One key component of the evaluation process is the ability to compare and rank methods. However, ranking classifiers and accurately comparing their performances, especially when taking application-specific preferences into account, remains challenging. For instance, commonly used evaluation tools like Receiver Operating Characteristic (ROC) and Precision/Recall (PR) spaces display performances based on two scores. Hence, they are inherently limited in their ability to compare classifiers across a broader range of scores and lack the capability to establish a clear ranking among classifiers. In this paper, we present a novel versatile tool, named the Tile, that organizes an infinity of ranking scores in a single 2D map for two-class classifiers, including common evaluation scores such as the accuracy, the true positive rate, the positive predictive value, Jaccard's coefficient, and all F-beta scores. Furthermore, we study the properties of the underlying ranking scores, such as the influence of the priors or the correspondences with the ROC space, and depict how to characterize any other score by comparing them to the Tile. Overall, we demonstrate that the Tile is a powerful tool that effectively captures all the rankings in a single visualization and allows interpreting them.
Related papers
- A Hitchhiker's Guide to Understanding Performances of Two-Class Classifiers [9.140821292601458]
We present a first hitchhiker's guide for understanding the performances of two-class classifiers by presenting four scenarios.
We leverage the newly introduced Tile tool and the different flavors to rank and analyze the performances of 74 state-of-the-art semantic segmentation models.
arXiv Detail & Related papers (2024-12-05T17:52:35Z) - Foundations of the Theory of Performance-Based Ranking [10.89980029564174]
This paper establishes the foundations of a universal theory for performance-based ranking.
We introduce a rigorous framework built on top of both the probability and order theories.
We show, in the case of two-class classification, that the family of ranking scores encompasses well-known performance scores.
arXiv Detail & Related papers (2024-12-05T15:05:25Z) - 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) - Fine-Grained Visual Classification using Self Assessment Classifier [12.596520707449027]
Extracting discriminative features plays a crucial role in the fine-grained visual classification task.
In this paper, we introduce a Self Assessment, which simultaneously leverages the representation of the image and top-k prediction classes.
We show that our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets.
arXiv Detail & Related papers (2022-05-21T07:41:27Z) - Decision Making for Hierarchical Multi-label Classification with
Multidimensional Local Precision Rate [4.812468844362369]
We introduce a new statistic called the multidimensional local precision rate (mLPR) for each object in each class.
We show that classification decisions made by simply sorting objects across classes in descending order of their mLPRs can, in theory, ensure the class hierarchy.
In response, we introduce HierRank, a new algorithm that maximizes an empirical version of CATCH using estimated mLPRs while respecting the hierarchy.
arXiv Detail & Related papers (2022-05-16T17:43:35Z) - Integrating Rankings into Quantized Scores in Peer Review [61.27794774537103]
In peer review, reviewers are usually asked to provide scores for the papers.
To mitigate this issue, conferences have started to ask reviewers to additionally provide a ranking of the papers they have reviewed.
There are no standard procedure for using this ranking information and Area Chairs may use it in different ways.
We take a principled approach to integrate the ranking information into the scores.
arXiv Detail & Related papers (2022-04-05T19:39:13Z) - Prototypical Classifier for Robust Class-Imbalanced Learning [64.96088324684683]
We propose textitPrototypical, which does not require fitting additional parameters given the embedding network.
Prototypical produces balanced and comparable predictions for all classes even though the training set is class-imbalanced.
We test our method on CIFAR-10LT, CIFAR-100LT and Webvision datasets, observing that Prototypical obtains substaintial improvements compared with state of the arts.
arXiv Detail & Related papers (2021-10-22T01:55:01Z) - Learning and Evaluating Representations for Deep One-class
Classification [59.095144932794646]
We present a two-stage framework for deep one-class classification.
We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations.
In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks.
arXiv Detail & Related papers (2020-11-04T23:33:41Z) - Predicting Classification Accuracy When Adding New Unobserved Classes [8.325327265120283]
We study how a classifier's performance can be used to extrapolate its expected accuracy on a larger, unobserved set of classes.
We formulate a robust neural-network-based algorithm, "CleaneX", which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes.
arXiv Detail & Related papers (2020-10-28T14:37:25Z) - 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) - Fine-Grained Visual Classification with Efficient End-to-end
Localization [49.9887676289364]
We present an efficient localization module that can be fused with a classification network in an end-to-end setup.
We evaluate the new model on the three benchmark datasets CUB200-2011, Stanford Cars and FGVC-Aircraft.
arXiv Detail & Related papers (2020-05-11T14:07:06Z)
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.