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 41–50 of 903 papers

TitleStatusHype
Uncertainty-aware abstention in medical diagnosis based on medical texts—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
A procedure for assessing of machine health index data prediction quality—0
Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification—0
MAQInstruct: Instruction-based Unified Event Relation Extraction—0
Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Learning to Help in Multi-Class Settings—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and ApplicationsCode0
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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