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 391–400 of 903 papers

TitleStatusHype
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
TagRec++: Hierarchical Label Aware Attention Network for Question CategorizationCode0
Retrieval of surgical phase transitions using reinforcement learning—0
Factorizable Joint Shift in Multinomial Classification—0
A novel Deep Learning approach for one-step Conformal Prediction approximationCode0
Deep Sequence Models for Text Classification Tasks—0
Learning Mutual Fund Categorization using Natural Language Processing—0
Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk RegularizationCode0
University of Bucharest Team at Semeval-2022 Task4: Detection and Classification of Patronizing and Condescending Language—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