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 226–250 of 903 papers

TitleStatusHype
Ensembling Uncertainty Measures to Improve Safety of Black-Box ClassifiersCode0
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks—0
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems—0
Feature-aware conditional GAN for category text generation—0
Predicting delays in Indian lower courts using AutoML and Decision ForestsCode0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
Characterizing Data Point Vulnerability via Average-Case RobustnessCode0
Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains—0
Eye Disease Classification Using Deep Learning Techniques—0
Mood Classification of Bangla Songs Based on Lyrics—0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Improving Primate Sounds Classification using Binary Presorting for Deep Learning—0
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?—0
Kalman Filter for Online Classification of Non-Stationary Data—0
A Generalized Unbiased Risk Estimator for Learning with Augmented ClassesCode0
Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks—0
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem—0
A generalized framework to predict continuous scores from medical ordinal labelsCode0
Entailment as Robust Self-LearnerCode1
Learning across Data Owners with Joint Differential Privacy—0
TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification—0
Solar Active Region Magnetogram Image Dataset for Studies of Space Weather—0
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code ClassificationCode0
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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