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 301–325 of 903 papers

TitleStatusHype
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis—0
Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques—0
Deep Sequence Models for Text Classification Tasks—0
Deep reinforced active learning for multi-class image classification—0
Automatic Identification and Classification of Bragging in Social Media—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net—0
A comprehensive solution to retrieval-based chatbot construction—0
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy—0
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks—0
Deep Multi Label Classification in Affine Subspaces—0
Automatic Classification of Functional Gait Disorders—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—0
Automated Multi-Label Classification based on ML-Plan—0
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data—0
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Deep Attention Model for Triage of Emergency Department Patients—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—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