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 211–220 of 903 papers

TitleStatusHype
Critical Review for One-class Classification: recent advances and the reality behind them—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Revisiting Classification Perspective on Scene Text Recognition—0
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations—0
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Cut your Losses with Squentropy—0
CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks—0
Data-dependent Generalization Bounds for Multi-class Classification—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified