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

Showing 851860 of 903 papers

TitleStatusHype
Interval Abstractions for Robust Counterfactual ExplanationsCode0
Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and ApplicationsCode0
A Masked Face Classification Benchmark on Low-Resolution Surveillance ImagesCode0
Deep attention-based classification network for robust depth predictionCode0
Inverse-Category-Frequency based supervised term weighting scheme for text categorizationCode0
Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language ProcessingCode0
Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual EmbeddingsCode0
DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast CancerCode0
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot LearningCode0
Predicting delays in Indian lower courts using AutoML and Decision ForestsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2Unverified
#ModelMetricClaimedVerifiedStatus
1COVID-ResNetF1 score0.9Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99Unverified