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 671–680 of 903 papers

TitleStatusHype
Multi-layer Domain Adaptation for Deep Convolutional Networks—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
The efficacy of various machine learning models for multi-class classification of RNA-seq expression data—0
Shallow Domain Adaptive Embeddings for Sentiment Analysis—0
Optimal multiclass overfitting by sequence reconstruction from Hamming queries—0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
GraphX^NET- Chest X-Ray Classification Under Extreme Minimal Supervision—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified