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 626–650 of 903 papers

TitleStatusHype
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
A comprehensive solution to retrieval-based chatbot construction—0
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
Adaptive Multinomial Matrix Completion—0
A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements—0
A Deep Generative Approach to Native Language Identification—0
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification—0
Adversarial Multi-Binary Neural Network for Multi-class Classification—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
A Fully Memristive Spiking Neural Network with Unsupervised Learning—0
A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification—0
A Generalization Error Bound for Multi-class Domain Generalization—0
A generalized flow for multi-class and binary classification tasks: An Azure ML approach—0
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling—0
A High Speed Multi-label Classifier based on Extreme Learning Machines—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