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 801–825 of 903 papers

TitleStatusHype
Application of SsVGMM to medical data-classification with novelty detectionCode0
TAPAS: Two-pass Approximate Adaptive Sampling for Softmax—0
Using Ranking-CNN for Age Estimation—0
Data-dependent Generalization Bounds for Multi-class Classification—0
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation—0
Generative-Discriminative Variational Model for Visual Recognition—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—0
Feature Incay for Representation Regularization—0
Semantic Softmax Loss for Zero-Shot Learning—0
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML—0
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response—0
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear LossCode0
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations—0
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks—0
Randomized Kernel Methods for Least-Squares Support Vector Machines—0
Computer Aided Detection of Anemia-like Pallor—0
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy—0
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text ClassificationCode0
GenSVM: A Generalized Multiclass Support Vector MachineCode0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—0
Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention—0
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest—0
Big Models for Big Data using Multi objective averaged one dependence estimators—0
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation—0
Post Selection Inference with Kernels—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