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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 671–680 of 903 papers

TitleStatusHype
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
GraphX^NET- Chest X-Ray Classification Under Extreme Minimal Supervision—0
Deep Multi Label Classification in Affine Subspaces—0
Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification—0
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification—0
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit—0
Progressive Fashion Attribute Extraction—0
AMF: Aggregated Mondrian Forests for Online LearningCode0
Binary Stochastic Representations for Large Multi-class Classification—0
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy—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