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 151–175 of 903 papers

TitleStatusHype
Critical Review for One-class Classification: recent advances and the reality behind them—0
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Decentralized Online Learning with Kernels—0
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster—0
A BERT-based Unsupervised Grammatical Error Correction Framework—0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—0
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography—0
A High Speed Multi-label Classifier based on Extreme Learning Machines—0
Convolutional Neural Networks in Multi-Class Classification of Medical Data—0
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields—0
Adaptive Multinomial Matrix Completion—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
A procedure for assessing of machine health index data prediction quality—0
A priori estimates for classification problems using neural networks—0
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling—0
Convergence rates of sub-sampled Newton methods—0
Correlation-based construction of neighborhood and edge features—0
COFGA: Classification Of Fine-Grained Features In Aerial Images—0
A pragmatic approach to multi-class classification—0
200K+ Crowdsourced Political Arguments for a New Chilean Constitution—0
Contrastive Learning for Fair Representations—0
CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Cognitive Radar Antenna Selection via Deep Learning—0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—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