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 276–300 of 903 papers

TitleStatusHype
Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet modelCode0
Fuzzy granular approximation classifierCode0
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesCode0
Enhanced Network Embedding with Text InformationCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph AttentionCode0
Semi-supervised Vector-valued Learning: Improved Bounds and AlgorithmsCode0
Optimal-margin evolutionary classifierCode0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities—0
Benchmarking deep learning models for bearing fault diagnosis using the CWRU dataset: A multi-label approach—0
Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture—0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss—0
An Effective Approach for Multi-label Classification with Missing Labels—0
Detecting immune cells with label-free two-photon autofluorescence and deep learning—0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
Described Spatial-Temporal Video Detection—0
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
Dermoscopic Image Analysis for ISIC Challenge 2018—0
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification—0
Degrees of Freedom in Deep Neural Networks—0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—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