SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

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Showing 571–580 of 903 papers

TitleStatusHype
The efficacy of various machine learning models for multi-class classification of RNA-seq expression data—0
The Fourier Discrepancy Function—0
Theoretical Analysis of Adversarial Learning: A Minimax Approach—0
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention—0
The SAMME.C2 algorithm for severely imbalanced multi-class classification—0
The Tree Loss: Improving Generalization with Many Classes—0
The Utility of General Domain Transfer Learning for Medical Language Tasks—0
ThyroidEffi 1.0: A Cost-Effective System for High-Performance Multi-Class Thyroid Carcinoma Classification—0
Tight Risk Bounds for Multi-Class Margin Classifiers—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified