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 426–450 of 903 papers

TitleStatusHype
Cross-domain Recommendation via Deep Domain Adaptation—0
Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities—0
Revisiting Classification Perspective on Scene Text Recognition—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Increasing Fairness via Combination with Learning Guarantees—0
Incremental user embedding modeling for personalized text classification—0
Inducing a hierarchy for multi-class classification problems—0
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter—0
CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Data-dependent Generalization Bounds for Multi-class Classification—0
Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Dynamic Spectrum Matching with One-shot Learning—0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images—0
Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Introducing the DOME Activation Functions—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Dynamic Sentence Boundary Detection for Simultaneous Translation—0
Biomarker based Cancer Classification using an Ensemble with Pre-trained Models—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines—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
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified