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 201–225 of 903 papers

TitleStatusHype
Noise-Free Explanation for Driving Action PredictionCode0
Non-Robust Features are Not Always Useful in One-Class Classification—0
Described Spatial-Temporal Video Detection—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors—0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest—0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Biomarker based Cancer Classification using an Ensemble with Pre-trained Models—0
Genetic Column Generation for Computing Lower Bounds for Adversarial Classification—0
Sequential Binary Classification for Intrusion Detection—0
Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions—0
kNN Classification of Malware Data Dependency Graph Features—0
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification—0
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Domain Adaptation with Cauchy-Schwarz DivergenceCode0
Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry PredictionCode0
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?—0
Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language ProcessingCode0
Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection MethodCode0
A Universal Growth Rate for Learning with Smooth Surrogate Losses—0
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing—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
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1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified