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 651–675 of 903 papers

TitleStatusHype
A multi-class structured dictionary learning method using discriminant atom selection—0
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization—0
A multi-label, dual-output deep neural network for automated bug triaging—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—0
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams—0
An Effective Approach for Multi-label Classification with Missing Labels—0
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning—0
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms—0
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data—0
A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks—0
A novel online multi-label classifier for high-speed streaming data applications—0
A Novel Online Real-time Classifier for Multi-label Data Streams—0
A Novel Progressive Learning Technique for Multi-class Classification—0
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network—0
An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text—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
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1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified