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 626–650 of 903 papers

TitleStatusHype
Improving automated segmentation of radio shows with audio embeddings—0
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Multi-class Gaussian Process Classification with Noisy InputsCode1
Transparency Promotion with Model-Agnostic Linear Competitors—0
Optimistic bounds for multi-output learning—0
Competing Ratio Loss for Discriminative Multi-class Image ClassificationCode0
An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text—0
Scalable Fine-grained Generated Image Classification Based on Deep Metric Learning—0
Sequential Classification with Empirically Observed Statistics—0
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements—0
Primal-Dual Block Generalized Frank-WolfeCode0
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks—0
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification—0
Geolocation with Attention-Based Multitask Learning Models—0
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text—0
A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification—0
Calibration tests in multi-class classification: A unifying frameworkCode0
Sparse Orthogonal Variational Inference for Gaussian ProcessesCode1
Global Capacity Measures for Deep ReLU Networks via Path Sampling—0
Cascading Machine Learning to Attack Bitcoin Anonymity—0
A multi-label, dual-output deep neural network for automated bug triaging—0
SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media TextCode0
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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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified