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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 281–290 of 903 papers

TitleStatusHype
Enhanced Network Embedding with Text InformationCode0
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear LossCode0
Imbalance Learning for Variable Star ClassificationCode0
Noise-Free Explanation for Driving Action PredictionCode0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities—0
Benchmarking deep learning models for bearing fault diagnosis using the CWRU dataset: A multi-label approach—0
Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture—0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss—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