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 476–500 of 903 papers

TitleStatusHype
Can multi-label classification networks know what they don’t know?Code1
Transfer learning approach to Classify the X-ray image that corresponds to corona disease Using ResNet50 pretrained by ChexNetCode0
Leveraging Classification Metrics for Quantitative System-Level Analysis with Temporal Logic Specifications—0
Segmentation of Anatomical Layers and Artifacts in Intravascular Polarization Sensitive Optical Coherence Tomography Using Attending Physician and Boundary Cardinality LossesCode0
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation—0
Meta-Cal: Well-controlled Post-hoc Calibration by RankingCode0
MARL: Multimodal Attentional Representation Learning for Disease Prediction—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
Self-supervised Spatial Reasoning on Multi-View Line DrawingsCode1
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification DatasetsCode1
Conclusive Local Interpretation Rules for Random ForestsCode0
Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
Robust Adversarial Classification via Abstaining—0
Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional NetworksCode0
Confidence Calibration for Domain Generalization under Covariate Shift—0
CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks—0
Generating CCG CategoriesCode0
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs—0
Simpson's Bias in NLP Training—0
ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets—0
Self-supervised Mean Teacher for Semi-supervised Chest X-ray ClassificationCode0
Calibrated simplex-mapping classificationCode0
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain—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