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 251–275 of 903 papers

TitleStatusHype
Financial Data Analysis with Robust Federated Logistic RegressionCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
An Integer Linear Programming Framework for Mining Constraints from DataCode0
Exponentially Convergent Algorithms for Supervised Matrix FactorizationCode0
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set ClassificationCode0
Extrapolating Expected Accuracies for Large Multi-Class ProblemsCode0
Federated Learning with Only Positive LabelsCode0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear LossCode0
Concise Explanations of Neural Networks using Adversarial TrainingCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Domain Adaptation with Cauchy-Schwarz DivergenceCode0
DOLDA - a regularized supervised topic model for high-dimensional multi-class regressionCode0
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in HindiCode0
An Exploration of Softmax Alternatives Belonging to the Spherical Loss FamilyCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Evaluating approaches for supervised semantic labelingCode0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Distance Guided Generative Adversarial Network for Explainable Binary ClassificationsCode0
Efficient Robust Optimal Transport with Application to Multi-Label ClassificationCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
A novel Deep Learning approach for one-step Conformal Prediction approximationCode0
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic RegressionCode0
Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time SeriesCode0
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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