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 26–50 of 903 papers

TitleStatusHype
Measuring Online Hate on 4chan using Pre-trained Deep Learning Models—0
SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-DetectionCode0
Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining—0
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages—0
iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed SpeciesCode3
Quantum Complex-Valued Self-Attention Model—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Probabilistic Quantum SVM Training on Ising Machine—0
Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
KréyoLID From Language Identification Towards Language MiningCode0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Predicting Cascading Failures in Power Systems using Machine Learning—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster—0
Uncertainty-aware abstention in medical diagnosis based on medical texts—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
A procedure for assessing of machine health index data prediction quality—0
Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification—0
MAQInstruct: Instruction-based Unified Event Relation Extraction—0
Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Learning to Help in Multi-Class Settings—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and ApplicationsCode0
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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