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 751–775 of 903 papers

TitleStatusHype
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
Okapi: Generalising Better by Making Statistical Matches MatchCode0
TagRec: Automated Tagging of Questions with Hierarchical Learning TaxonomyCode0
Looking back at Labels: A Class based Domain Adaptation TechniqueCode0
Lovasz Convolutional NetworksCode0
Vision-based Estimation of Fatigue and Engagement in Cognitive Training SessionsCode0
Federated Learning with Only Positive LabelsCode0
Machine and Deep Learning Applications to Mouse Dynamics for Continuous User AuthenticationCode0
TagRec++: Hierarchical Label Aware Attention Network for Question CategorizationCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
Additive interaction modelling using I-priorsCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic RegressionCode0
Machine Learning Methods for Track Classification in the AT-TPCCode0
A novel Deep Learning approach for one-step Conformal Prediction approximationCode0
Calibrated simplex-mapping classificationCode0
Domain Adaptation with Cauchy-Schwarz DivergenceCode0
An Exploration of Softmax Alternatives Belonging to the Spherical Loss FamilyCode0
Conclusive Local Interpretation Rules for Random ForestsCode0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label ClassificationCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
AutoMSC: Automatic Assignment of Mathematics Subject Classification LabelsCode0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
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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
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified