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 701–725 of 903 papers

TitleStatusHype
Neural-based Tamil Grammar Error Detection—0
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit—0
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?—0
Neural Neighborhood Encoding for Classification—0
Neural Network Learning and Quantum Gravity—0
New Bounds on the Accuracy of Majority Voting for Multi-Class Classification—0
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning—0
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest—0
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference—0
Non-Robust Features are Not Always Useful in One-Class Classification—0
Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions—0
Obfuscated Memory Malware Detection—0
Occupant's Behavior and Emotion Based Indoor Environment's Illumination Regulation—0
Ocular Diseases Diagnosis in Fundus Images using a Deep Learning: Approaches, tools and Performance evaluation—0
OffendES: A New Corpus in Spanish for Offensive Language Research—0
Ensembling Uncertainty Measures to Improve Safety of Black-Box ClassifiersCode0
An Integer Linear Programming Framework for Mining Constraints from DataCode0
Enhanced Network Embedding with Text InformationCode0
A hybrid algorithm for Bayesian network structure learning with application to multi-label learningCode0
Learning Robust Sequential Recommenders through Confident Soft LabelsCode0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Noise-Free Explanation for Driving Action PredictionCode0
Evaluating approaches for supervised semantic labelingCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Systematic Evaluation of Predictive FairnessCode0
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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
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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