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 501–525 of 903 papers

TitleStatusHype
Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease—0
A comprehensive solution to retrieval-based chatbot construction—0
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis—0
Sum of Ranked Range Loss for Supervised LearningCode0
Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensionsCode0
Gaussian Processes on Hypergraphs—0
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021—0
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data—0
Scalable Cross Validation Losses for Gaussian Process Models—0
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
Meta-Cal: Well-controlled Post-hoc Calibration by RankingCode0
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation—0
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
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
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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