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 111–120 of 903 papers

TitleStatusHype
Light Weight CNN for classification of Brain Tumors from MRI Images—0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
ThyroidEffi 1.0: A Cost-Effective System for High-Performance Multi-Class Thyroid Carcinoma Classification—0
Multi-output Classification Framework and Frequency Layer Normalization for Compound Fault Diagnosis in Motor—0
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network—0
Quantum neural networks facilitating quantum state classification—0
PRACH Preamble Detection as a Multi-Class Classification Problem: A Machine Learning Approach Using SVM—0
Leveraging Embedding Techniques in Multimodal Machine Learning for Mental Illness Assessment—0
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
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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