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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 841–850 of 903 papers

TitleStatusHype
Degrees of Freedom in Deep Neural Networks—0
A generalized flow for multi-class and binary classification tasks: An Azure ML approach—0
Active Learning from Positive and Unlabeled DataCode0
Toward Optimal Feature Selection in Naive Bayes for Text Categorization—0
DOLDA - a regularized supervised topic model for high-dimensional multi-class regressionCode0
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss—0
A pragmatic approach to multi-class classification—0
A simple technique for improving multi-class classification with neural networks—0
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking—0
Semi-Supervised Zero-Shot Classification With Label Representation Learning—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