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 176–200 of 903 papers

TitleStatusHype
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems—0
A procedure for assessing of machine health index data prediction quality—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
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—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
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster—0
Data-dependent Generalization Bounds for Multi-class Classification—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits—0
Confidence Prediction for Lexicon-Free OCR—0
A scalable stage-wise approach to large-margin multi-class loss based boosting—0
Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients—0
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods—0
Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration—0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Contrastive Learning for Fair Representations—0
Cut your Losses with Squentropy—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