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 101–125 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
Noise-Free Explanation for Driving Action PredictionCode0
Described Spatial-Temporal Video Detection—0
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
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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