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 621–630 of 903 papers

TitleStatusHype
On the computational complexity of the probabilistic label tree algorithms—0
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge—0
200K+ Crowdsourced Political Arguments for a New Chilean Constitution—0
Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration—0
3D Shape Classification Using Collaborative Representation based Projections—0
Aanisha@TamilNLP-ACL2022:Abusive Detection in Tamil—0
A Bayesian Approach for Accurate Classification-Based Aggregates—0
A BERT-based Unsupervised Grammatical Error Correction Framework—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified