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 251–275 of 903 papers

TitleStatusHype
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
Probabilistic Truly Unordered Rule SetsCode0
3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar dataCode0
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERTCode0
Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land Cover Mapping—0
Safe reinforcement learning in uncertain contextsCode0
Distance Guided Generative Adversarial Network for Explainable Binary ClassificationsCode0
Reading Between the Leads: Local Lead-Attention Based Classification of Electrocardiogram SignalsCode0
Aspect category learning and sentimental analysis using weakly supervised learning—0
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image DeformationsCode0
NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide ImagesCode0
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class ClassificationCode0
Llama Guard: LLM-based Input-Output Safeguard for Human-AI ConversationsCode0
Improving Bias Mitigation through Bias Experts in Natural Language UnderstandingCode0
Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through Dialect Identification using Transformer-based Approach—0
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses—0
PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification—0
Exponentially Convergent Algorithms for Supervised Matrix FactorizationCode0
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning—0
Image Classification using Combination of Topological Features and Neural Networks—0
Auto deep learning for bioacoustic signalsCode0
Understanding Deep Representation Learning via Layerwise Feature Compression and DiscriminationCode0
Learning Robust Sequential Recommenders through Confident Soft LabelsCode0
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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