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 451–500 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
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks—0
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Introducing the DOME Activation Functions—0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Multi-class Probabilistic Bounds for Self-learning—0
Violence Detection in Videos—0
Predicting Loss Risks for B2B Tendering Processes—0
On the Usefulness of Personality Traits in Opinion-oriented Tasks—0
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class ClassificationCode0
OffendES: A New Corpus in Spanish for Offensive Language Research—0
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method—0
A Survey on Open Set Recognition—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators—0
SphereFace2: Binary Classification is All You Need for Deep Face Recognition—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
E-PixelHop: An Enhanced PixelHop Method for Object Classification—0
Tropical cyclone intensity estimations over the Indian ocean using Machine Learning—0
TagRec: Automated Tagging of Questions with Hierarchical Learning TaxonomyCode0
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Multi-Class Classification from Single-Class Data with Confidences—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—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