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 811–820 of 903 papers

TitleStatusHype
Dynamic Sentence Boundary Detection for Simultaneous Translation—0
Dynamic Spectrum Matching with One-shot Learning—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—0
EC3: Combining Clustering and Classification for Ensemble Learning—0
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Elimination of All Bad Local Minima in Deep Learning—0
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified