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 301–310 of 903 papers

TitleStatusHype
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis—0
Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques—0
Deep Sequence Models for Text Classification Tasks—0
Deep reinforced active learning for multi-class image classification—0
Automatic Identification and Classification of Bragging in Social Media—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net—0
A comprehensive solution to retrieval-based chatbot construction—0
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy—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