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 251–275 of 903 papers

TitleStatusHype
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture—0
Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities—0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Classification with many classes: challenges and pluses—0
A generalized flow for multi-class and binary classification tasks: An Azure ML approach—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Classification based on Topological Data Analysis—0
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data—0
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text—0
A Generalization Error Bound for Multi-class Domain Generalization—0
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Ensemble-based Adversarial Defense Using Diversified Distance Mapping—0
Event-Event Relation Extraction using Probabilistic Box Embedding—0
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays—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
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
Cascading Machine Learning to Attack Bitcoin Anonymity—0
A Novel Progressive Learning Technique for Multi-class Classification—0
A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification—0
Capsules as viewpoint learners for human pose estimation—0
A Novel Online Real-time Classifier for Multi-label Data Streams—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
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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