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 621–630 of 903 papers

TitleStatusHype
Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design—0
Learning Semantic Similarities for Prototypical Classifiers—0
Learning to Help in Multi-Class Settings—0
Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment—0
Leveraging Cascaded Binary Classification and Multimodal Fusion for Dementia Detection through Spontaneous Speech—0
Leveraging Classification Metrics for Quantitative System-Level Analysis with Temporal Logic Specifications—0
Leveraging Embedding Techniques in Multimodal Machine Learning for Mental Illness Assessment—0
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing—0
Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG—0
Light Weight CNN for classification of Brain Tumors from MRI Images—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
#ModelMetricClaimedVerifiedStatus
1COVID-ResNetF1 score0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified