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 126–150 of 903 papers

TitleStatusHype
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
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis—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
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing—0
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning—0
ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks—0
Interval Abstractions for Robust Counterfactual ExplanationsCode0
Multiclass ROC—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