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 726–750 of 903 papers

TitleStatusHype
Enhanced Network Embedding with Text InformationCode0
MEMOIR: Multi-class Extreme Classification with Inexact Margin—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Single-Label Multi-Class Image Classification by Deep Logistic Regression—0
Streaming Network Embedding through Local Actions—0
Theoretical Analysis of Adversarial Learning: A Minimax Approach—0
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification—0
Automated Multi-Label Classification based on ML-Plan—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates—0
DCSVM: Fast Multi-class Classification using Support Vector Machines—0
Machine Learning Methods for Track Classification in the AT-TPCCode0
Concise Explanations of Neural Networks using Adversarial TrainingCode0
Multi-class Classification Model Inspired by Quantum Detection Theory—0
Improved Generalization Bounds for Adversarially Robust Learning—0
Phrase-level Self-Attention Networks for Universal Sentence Encoding—0
Model Transfer with Explicit Knowledge of the Relation between Class Definitions—0
Unsupervised Adversarial Invariance—0
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference—0
Solving for multi-class: a survey and synthesis—0
Semi-Supervised Deep Learning with MemoryCode0
COFGA: Classification Of Fine-Grained Features In Aerial Images—0
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data—0
Filtering Aggression from the Multilingual Social Media Feed—0
Punctuation as Native Language Interference—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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified