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 101–110 of 903 papers

TitleStatusHype
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net—0
A comprehensive solution to retrieval-based chatbot construction—0
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
A Deep Generative Approach to Native Language Identification—0
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge—0
Bridging Social Media via Distant Supervision—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
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified