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 381–390 of 903 papers

TitleStatusHype
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Geolocation with Attention-Based Multitask Learning Models—0
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—0
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification—0
GJG@TamilNLP-ACL2022: Using Transformers for Abusive Comment Classification in Tamil—0
Global Capacity Measures for Deep ReLU Networks via Path Sampling—0
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data—0
EC3: Combining Clustering and Classification for Ensemble Learning—0
Granular Ball K-Class Twin Support Vector Classifier—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—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
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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