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 631–640 of 903 papers

TitleStatusHype
A comprehensive solution to retrieval-based chatbot construction—0
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
Adaptive Multinomial Matrix Completion—0
A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements—0
A Deep Generative Approach to Native Language Identification—0
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net—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