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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 903 papers

TitleStatusHype
Relationships are Complicated! An Analysis of Relationships Between Datasets on the WebCode4
Obfuscated Memory Malware Detection—0
Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and MethodCode0
Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases—0
pSVM: Soft-margin SVMs with p-norm Hinge LossCode0
Neural CRNs: A Natural Implementation of Learning in Chemical Reaction NetworksCode0
SCREENER: A general framework for task-specific experiment design in quantitative MRI—0
Graph Residual based Method for Molecular Property Prediction—0
Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification TasksCode1
On the Utility of Speech and Audio Foundation Models for Marmoset Call AnalysisCode0
Regression under demographic parity constraints via unlabeled post-processing—0
Benchmarking deep learning models for bearing fault diagnosis using the CWRU dataset: A multi-label approach—0
Enhanced H-Consistency Bounds—0
Word Embedding Dimension Reduction via Weakly-Supervised Feature SelectionCode0
Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild DatasetCode1
Weighted Aggregation of Conformity Scores for Classification—0
Non-Robust Features are Not Always Useful in One-Class Classification—0
Described Spatial-Temporal Video Detection—0
Noise-Free Explanation for Driving Action PredictionCode0
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo MethodsCode1
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
A data-centric approach for assessing progress of Graph Neural NetworksCode1
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
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
Show:102550
← PrevPage 3 of 19Next →
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