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 726–750 of 903 papers

TitleStatusHype
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays—0
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
Classification based on Topological Data Analysis—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Classification with many classes: challenges and pluses—0
Classified as unknown: A novel Bayesian neural network—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—0
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations—0
Class-Imbalanced Complementary-Label Learning via Weighted Loss—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits—0
COFGA: Classification Of Fine-Grained Features In Aerial Images—0
Cognitive Radar Antenna Selection via Deep Learning—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems—0
Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases—0
Combining Task Predictors via Enhancing Joint Predictability—0
Comment on Is Complexity an Illusion?—0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response—0
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses—0
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models—0
Computer Aided Detection of Anemia-like Pallor—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—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