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
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning—0
Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise—0
University of Bucharest Team at Semeval-2022 Task4: Detection and Classification of Patronizing and Condescending Language—0
Unsupervised Adversarial Invariance—0
Upper bounds on the Natarajan dimensions of some function classes—0
Using Ranking-CNN for Age Estimation—0
Utilizing Weak Supervision To Generate Indonesian Conservation Dataset—0
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis—0
Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement—0
Violence Detection in Videos—0
Visualizing CoAtNet Predictions for Aiding Melanoma Detection—0
Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts—0
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa—0
Weighted Aggregation of Conformity Scores for Classification—0
When Can Memorization Improve Fairness?—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model for Short Text Multi-class Classification Problems—0
Wound Severity Classification using Deep Neural Network—0
X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications—0
Yelp Dataset Challenge: Review Rating Prediction—0
Zero-shot Active Learning with Topological Clustering for Multiclass Classification—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
E-PixelHop: An Enhanced PixelHop Method for Object Classification—0
Error-Correcting Factorization—0
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks—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