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 101–125 of 903 papers

TitleStatusHype
Analysis of Zero Day Attack Detection Using MLP and XAI—0
A comprehensive solution to retrieval-based chatbot construction—0
Classification based on Topological Data Analysis—0
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
A Deep Generative Approach to Native Language Identification—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Automated Multi-Label Classification based on ML-Plan—0
Automatic Classification of Functional Gait Disorders—0
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks—0
Automatic Identification and Classification of Bragging in Social Media—0
Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques—0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—0
A Universal Growth Rate for Learning with Smooth Surrogate Losses—0
A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—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