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 581–590 of 903 papers

TitleStatusHype
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis—0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images—0
Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI—0
Introducing the DOME Activation Functions—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
Is Encoder-Decoder Transformer the Shiny Hammer?—0
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?—0
Iterative Weak Learnability and Multi-Class AdaBoost—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