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 401450 of 903 papers

TitleStatusHype
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays0
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and AdaptivityCode0
The SAMME.C2 algorithm for severely imbalanced multi-class classification0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Contrastive Learning for Fair Representations0
Flat and Nested Negation and Uncertainty Detection with PubMed BERT0
Logically at Factify 2022: Multimodal Fact Verification0
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 Detection0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT0
On the Value of Interaction and Function Approximation in Imitation Learning0
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding0
Event-Event Relation Extraction using Probabilistic Box Embedding0
Margin-Independent Online Multiclass Learning via Convex Geometry0
On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications0
Critical Sentence Identification in Legal Cases Using Multi-Class Classification0
A Topological Data Analysis Based ClassifierCode0
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning0
Sexism Identification in Tweets and Gabs using Deep Neural Networks0
Co-attention network with label embedding for text classificationCode1
On the Effectiveness of Interpretable Feedforward Neural Network0
TorchXRayVision: A library of chest X-ray datasets and modelsCode2
Convergence of Uncertainty Sampling for Active Learning0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding0
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis0
Distribution-Free Federated Learning with Conformal Predictions0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Pairwise Margin Maximization for Deep Neural NetworksCode0
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks0
Tribuo: Machine Learning with Provenance in JavaCode2
Emoji Prediction from Twitter Data using Deep Learning ApproachCode1
LexGLUE: A Benchmark Dataset for Legal Language Understanding in EnglishCode1
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications0
Introducing the DOME Activation Functions0
Improving the Accuracy of Learning Example Weights for Imbalance Classification0
Exploiting Class Activation Value for Partial-Label LearningCode1
Can multi-label classification networks know what they don't know?Code1
Multi-class Probabilistic Bounds for Self-learning0
Multi-loss ensemble deep learning for chest X-ray classification0
Violence Detection in Videos0
Language Models are Few-shot Multilingual LearnersCode1
Predicting Loss Risks for B2B Tendering Processes0
OffendES: A New Corpus in Spanish for Offensive Language Research0
On the Usefulness of Personality Traits in Opinion-oriented Tasks0
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class ClassificationCode0
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method0
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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