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 451–475 of 903 papers

TitleStatusHype
A Survey on Open Set Recognition—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators—0
SphereFace2: Binary Classification is All You Need for Deep Face Recognition—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Clinical Relation Extraction Using Transformer-based ModelsCode1
Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack MusicCode1
Tropical cyclone intensity estimations over the Indian ocean using Machine Learning—0
E-PixelHop: An Enhanced PixelHop Method for Object Classification—0
TagRec: Automated Tagging of Questions with Hierarchical Learning TaxonomyCode0
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Multi-Class Classification from Single-Class Data with Confidences—0
Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
A comprehensive solution to retrieval-based chatbot construction—0
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis—0
Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensionsCode0
Sum of Ranked Range Loss for Supervised LearningCode0
Gaussian Processes on Hypergraphs—0
Dual-Objective Fine-Tuning of BERT for Entity MatchingCode1
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021—0
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data—0
Scalable Cross Validation Losses for Gaussian Process Models—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