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 451–475 of 903 papers

TitleStatusHype
Flat and Nested Negation and Uncertainty Detection with PubMed BERT—0
Logically at Factify 2022: Multimodal Fact Verification—0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep ClassifiersCode0
Neural-based Tamil Grammar Error Detection—0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—0
On the Value of Interaction and Function Approximation in Imitation Learning—0
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Event-Event Relation Extraction using Probabilistic Box Embedding—0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding—0
Margin-Independent Online Multiclass Learning via Convex Geometry—0
On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications—0
Critical Sentence Identification in Legal Cases Using Multi-Class Classification—0
A Topological Data Analysis Based ClassifierCode0
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning—0
Sexism Identification in Tweets and Gabs using Deep Neural Networks—0
On the Effectiveness of Interpretable Feedforward Neural Network—0
Convergence of Uncertainty Sampling for Active Learning—0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis—0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding—0
Distribution-Free Federated Learning with Conformal Predictions—0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Pairwise Margin Maximization for Deep Neural NetworksCode0
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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