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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
Utilizing Weak Supervision To Generate Indonesian Conservation Dataset—0
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label ClassificationCode0
Open-Set Knowledge-Based Visual Question Answering with Inference PathsCode0
Pool-Based Active Learning with Proper Topological Regions—0
QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers—0
A matter of attitude: Focusing on positive and active gradients to boost saliency mapsCode0
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
New Bounds on the Accuracy of Majority Voting for Multi-Class Classification—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