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 201–250 of 903 papers

TitleStatusHype
Convergence of Uncertainty Sampling for Active Learning—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
Convergence rates of sub-sampled Newton methods—0
Convolutional Neural Networks in Multi-Class Classification of Medical Data—0
Correlation-based construction of neighborhood and edge features—0
Counterfactual Explanations for Predictive Business Process Monitoring—0
COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images—0
Class-Imbalanced Complementary-Label Learning via Weighted Loss—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approach—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
Cross-domain Recommendation via Deep Domain Adaptation—0
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing—0
A Bayesian Approach for Accurate Classification-Based Aggregates—0
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Cut your Losses with Squentropy—0
CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks—0
Data-dependent Generalization Bounds for Multi-class Classification—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Data-driven root-cause analysis for distributed system anomalies—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
DCSVM: Fast Multi-class Classification using Support Vector Machines—0
DPPMask: Masked Image Modeling with Determinantal Point Processes—0
DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines—0
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations—0
A Deep Generative Approach to Native Language Identification—0
Deep Attention Model for Triage of Emergency Department Patients—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—0
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Automatic Classification of Functional Gait Disorders—0
Deep Multi Label Classification in Affine Subspaces—0
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks—0
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy—0
Deep reinforced active learning for multi-class image classification—0
Deep Sequence Models for Text Classification Tasks—0
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—0
Degrees of Freedom in Deep Neural Networks—0
Apple Counting using Convolutional Neural Networks—0
Dermoscopic Image Analysis for ISIC Challenge 2018—0
Classified as unknown: A novel Bayesian neural network—0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
Detecting immune cells with label-free two-photon autofluorescence and deep learning—0
Classification with many classes: challenges and pluses—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