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 601–650 of 903 papers

TitleStatusHype
Consistent Structured Prediction with Max-Min Margin Markov NetworksCode0
Deep brain state classification of MEG dataCode0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Dynamic Sentence Boundary Detection for Simultaneous Translation—0
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling—0
Probabilistic Classification Vector Machine for Multi-Class Classification—0
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa—0
An Integer Linear Programming Framework for Mining Constraints from DataCode0
Deep Layer-wise Networks Have Closed-Form Weights—0
Learnability with Indirect Supervision Signals—0
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measuresCode0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)Code0
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification—0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
On the Equivalence between Online and Private Learnability beyond Binary Classification—0
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices—0
Minimizing Supervision in Multi-label Categorization—0
AutoMSC: Automatic Assignment of Mathematics Subject Classification LabelsCode0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
On Using Transfer Learning For Plant Disease Detection—0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
Federated Learning with Only Positive LabelsCode0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from RadiographsCode0
Adversarial Multi-Binary Neural Network for Multi-class Classification—0
Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing—0
Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation—0
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift—0
On the Learning Property of Logistic and Softmax Losses for Deep Neural NetworksCode0
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses—0
Imbalance Learning for Variable Star ClassificationCode0
On the generalization of bayesian deep nets for multi-class classification—0
Optimistic bounds for multi-output prediction—0
The Utility of General Domain Transfer Learning for Medical Language Tasks—0
Multi-Class Classification from Noisy-Similarity-Labeled Data—0
Improving automated segmentation of radio shows with audio embeddings—0
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Optimistic bounds for multi-output learning—0
Transparency Promotion with Model-Agnostic Linear Competitors—0
Competing Ratio Loss for Discriminative Multi-class Image ClassificationCode0
An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text—0
Scalable Fine-grained Generated Image Classification Based on Deep Metric Learning—0
Sequential Classification with Empirically Observed Statistics—0
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements—0
Primal-Dual Block Generalized Frank-WolfeCode0
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks—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