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 526–550 of 903 papers

TitleStatusHype
Simpson's Bias in NLP Training—0
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs—0
ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets—0
Self-supervised Mean Teacher for Semi-supervised Chest X-ray ClassificationCode0
Calibrated simplex-mapping classificationCode0
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain—0
Set-valued classification -- overview via a unified framework—0
Revisiting Classification Perspective on Scene Text Recognition—0
Inducing a hierarchy for multi-class classification problems—0
Sentiment Analysis for YouTube Comments in Roman Urdu—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
Disease2Vec: Representing Alzheimer's Progression via Disease Embedding Tree—0
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification—0
Deep Learning with Label Differential Privacy—0
Classification based on Topological Data Analysis—0
The Fourier Discrepancy Function—0
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set ClassificationCode0
Iterative Weak Learnability and Multi-Class AdaBoost—0
Pitfalls of Assessing Extracted Hierarchies for Multi-Class Classification—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in HindiCode0
Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts—0
Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual EmbeddingsCode0
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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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified