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
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification—0
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text—0
Geolocation with Attention-Based Multitask Learning Models—0
A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification—0
Calibration tests in multi-class classification: A unifying frameworkCode0
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
Deep localization of protein structures in fluorescence microscopy imagesCode0
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks—0
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data—0
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised ClassificationCode0
How can we generalise learning distributed representations of graphs?—0
Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment—0
Scalable Gaussian Process Classification with Additive Noise for Various LikelihoodsCode0
Semi-supervised Vector-valued Learning: Improved Bounds and AlgorithmsCode0
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network—0
Student Performance Prediction with Optimum Multilabel Ensemble Model—0
Explicit Facial Expression Transfer via Fine-Grained Representations—0
Multi-layer Domain Adaptation for Deep Convolutional Networks—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
The efficacy of various machine learning models for multi-class classification of RNA-seq expression data—0
Shallow Domain Adaptive Embeddings for Sentiment Analysis—0
Optimal multiclass overfitting by sequence reconstruction from Hamming queries—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
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified