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 151–175 of 903 papers

TitleStatusHype
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—0
Diagnosis and Severity Assessment of Ulcerative Colitis using Self Supervised Learning—0
Granular Ball K-Class Twin Support Vector Classifier—0
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms—0
Learning from Concealed LabelsCode0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Bi-Band ECoGNet for ECoG Decoding on Classification Task—0
Training Multi-Layer Binary Neural Networks With Local Binary Error Signals—0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information—0
Energy-based features and bi-LSTM neural network for EEG-based music and voice classification—0
Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization—0
A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks—0
DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines—0
Model agnostic local variable importance for locally dependent relationships—0
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale—0
Generalization and Risk Bounds for Recurrent Neural Networks—0
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selection—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
Comment on Is Complexity an Illusion?—0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images—0
Multi-Class Abnormality Classification in Video Capsule Endoscopy Using Deep LearningCode0
Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement—0
Machine learning approach to brain tumor detection and classification—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