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 276–300 of 903 papers

TitleStatusHype
Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time SeriesCode0
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?—0
Performance Improvement in Multi-class Classification via Automated Hierarchy Generation and Exploitation through Extended LCPN Schemes—0
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT—0
Support matrix machine: A review—0
Neural Collapse in Multi-label Learning with Pick-all-label LossCode0
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms—0
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention—0
Powerset multi-class cross entropy loss for neural speaker diarization—0
Utilizing Weak Supervision To Generate Indonesian Conservation Dataset—0
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label ClassificationCode0
Open-Set Knowledge-Based Visual Question Answering with Inference PathsCode0
Pool-Based Active Learning with Proper Topological Regions—0
QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers—0
A matter of attitude: Focusing on positive and active gradients to boost saliency mapsCode0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN—0
New Bounds on the Accuracy of Majority Voting for Multi-Class Classification—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization—0
Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes—0
Spatial encoding of BOLD fMRI time series for categorizing static images across visual datasets: A pilot study on human visionCode0
Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette—0
Application of Quantum Pre-Processing Filter for Binary Image Classification with Small SamplesCode0
Ensembling Uncertainty Measures to Improve Safety of Black-Box ClassifiersCode0
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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