REVIEW 10 cited by
Metrics for Multi-Class Classification: an Overview
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Metrics for Multi-Class Classification: an Overview
read the original abstract
Classification tasks in machine learning involving more than two classes are known by the name of "multi-class classification". Performance indicators are very useful when the aim is to evaluate and compare different classification models or machine learning techniques. Many metrics come in handy to test the ability of a multi-class classifier. Those metrics turn out to be useful at different stage of the development process, e.g. comparing the performance of two different models or analysing the behaviour of the same model by tuning different parameters. In this white paper we review a list of the most promising multi-class metrics, we highlight their advantages and disadvantages and show their possible usages during the development of a classification model.
Forward citations
Cited by 10 Pith papers
-
UA-Legal-Bench: A Benchmark for Evaluating Large Language Models on Ukrainian Legal Reasoning
UA-Legal-Bench is a new five-task benchmark for Ukrainian legal reasoning that demonstrates task-dependent few-shot prompting effects and the need for macro-F1 over accuracy on imbalanced classes.
-
Online Set Learning from Precision and Recall Feedback
A hypothesis class is learnable in this online precision-recall feedback model if and only if it has finite VC dimension, with algorithms achieving regret bounds in realizable and agnostic settings despite ERM failing.
-
Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
TC-UMIA is a population-level attack using pre- and post-unlearning predictions to infer membership across forget, retain, and unseen sets, revealing added privacy leakage to retained data.
-
Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
Unlearning increases privacy leakage for the retain set, and a new tri-class membership inference attack distinguishes forget, retain, and unseen data using pre- and post-unlearning model outputs.
-
HADS-Net:A Hybrid Attention-Augmented Dual-Stream Network with Physics-Informed Augmentation for Breast Ultrasound Image Classification
HADS-Net fuses physics-informed texture features from EfficientNet-B3 with Sobel boundary features via cross-attention to reach 96.58% accuracy and 0.9978 macro ROC-AUC on the BUSI breast ultrasound dataset.
-
Solving Constrained Affine Heaviside Composite Optimization Problems by a Progressive IP Approach
A progressive IP method with successive decomposition and approximation solves constrained affine Heaviside composite optimization problems, with proven convergence to local optima and numerical support from classific...
-
Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model
Expert re-annotations of a German ABSA dataset serve as ground truth to evaluate how students, crowdworkers, and LLMs affect inter-annotator agreement and downstream performance on ACSA and TASD tasks using BERT, T5, ...
-
A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
-
Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging
A 4-qumode Gaussian CV-QNN classifies MedMNIST images with accuracy statistically indistinguishable from a 42-parameter classical linear model and a DV-QNN.
-
Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models
Fine-tuned VGG16, VGG19 and ResNet-50v2 classify a public Kaggle chest X-ray set (COVID-19, normal, viral pneumonia) at 85–96% accuracy; the abstract claims tuberculosis and lung-cancer coverage the experiments never include.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.