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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 461–470 of 903 papers

TitleStatusHype
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?—0
Pool-Based Active Learning with Proper Topological Regions—0
Pose Estimation Based on 3D Models—0
Post Selection Inference with Kernels—0
PowerGraph: A power grid benchmark dataset for graph neural networks—0
Powerset multi-class cross entropy loss for neural speaker diarization—0
PRACH Preamble Detection as a Multi-Class Classification Problem: A Machine Learning Approach Using SVM—0
Predicting Cascading Failures in Power Systems using Machine Learning—0
Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification—0
Predicting Loss Risks for B2B Tendering Processes—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