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 326–350 of 903 papers

TitleStatusHype
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer ClassificationCode0
Vision-based Estimation of Fatigue and Engagement in Cognitive Training SessionsCode0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
MisRoBÆRTa: Transformers versus MisinformationCode0
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge—0
Learning Optimal Fair Scoring Systems for Multi-Class Classification—0
KeyDetect --Detection of anomalies and user based on Keystroke Dynamics—0
A BERT-based Unsupervised Grammatical Error Correction Framework—0
Neuro-symbolic Rule Learning in Real-world Classification TasksCode0
A Novel Multi-Stage Approach for Hierarchical Intrusion DetectionCode0
Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques—0
Transformer Models for Acute Brain Dysfunction Prediction—0
DPPMask: Masked Image Modeling with Determinantal Point Processes—0
Machine learning tools to improve nonlinear modeling parameters of RC columns—0
Graph-based Extreme Feature Selection for Multi-class Classification Tasks—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?—0
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
Occupant's Behavior and Emotion Based Indoor Environment's Illumination Regulation—0
Optimal Transport for Change Detection on LiDAR Point CloudsCode0
Capsules as viewpoint learners for human pose estimation—0
Cut your Losses with Squentropy—0
Conformalized Semi-supervised Random Forest for Classification and Abnormality DetectionCode0
Classified as unknown: A novel Bayesian neural network—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified