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 776–800 of 903 papers

TitleStatusHype
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
On the Learning Property of Logistic and Softmax Losses for Deep Neural NetworksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Fuzzy granular approximation classifierCode0
SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-DetectionCode0
SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media TextCode0
On the Utility of Speech and Audio Foundation Models for Marmoset Call AnalysisCode0
AMF: Aggregated Mondrian Forests for Online LearningCode0
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesCode0
Generating CCG CategoriesCode0
Meta-Cal: Well-controlled Post-hoc Calibration by RankingCode0
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised ClassificationCode0
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image DeformationsCode0
GenSVM: A Generalized Multiclass Support Vector MachineCode0
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class ClassificationCode0
DOLDA - a regularized supervised topic model for high-dimensional multi-class regressionCode0
MisRoBÆRTa: Transformers versus MisinformationCode0
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measuresCode0
Open-Set Knowledge-Based Visual Question Answering with Inference PathsCode0
Optimal-margin evolutionary classifierCode0
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in HindiCode0
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer ClassificationCode0
Optimal Transport for Change Detection on LiDAR Point CloudsCode0
Towards Macro-AUC oriented Imbalanced Multi-Label Continual LearningCode0
More Consideration for the PerceptronCode0
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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