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Binary Classification

Papers

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Showing 1076–1100 of 2574 papers

TitleStatusHype
Generalized version of the support vector machine for binary classification problems: supporting hyperplane machine—0
Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection—0
Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification—0
Fine-Tuning BERTs for Definition Extraction from Mathematical Text—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
Class-Aware Adversarial Lung Nodule Synthesis in CT Images—0
A Fused Elastic Net Logistic Regression Model for Multi-Task Binary Classification—0
Class Activation Map Generation by Representative Class Selection and Multi-Layer Feature Fusion—0
Fine-Grained Bias Exploration and Mitigation for Group-Robust Classification—0
Movement-Prediction-Adjusted Naïve Forecast—0
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients—0
Can depth-adaptive BERT perform better on binary classification tasks—0
Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images—0
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models—0
Fixed Random Classifier Rearrangement for Continual Learning—0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis—0
Flow-based Detection of Botnets through Bio-inspired Optimisation of Machine Learning—0
Fock State-enhanced Expressivity of Quantum Machine Learning Models—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank—0
Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
Forecasting Drought Using Machine Learning in California—0
Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches—0
Fuzzy Least Squares Twin Support Vector Machines—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Trompt + OpenAI embeddingAUROC0.98—Unverified
2LightGBM + OpenAI embeddingAUROC0.97—Unverified
3FTTransformer + RoBERTa fintuneAUROC0.96—Unverified
4LightGBM + RoBERTa embeddingAUROC0.95—Unverified
5FTTransformer + RoBERTa embeddingAUROC0.94—Unverified
6ResNet + RoBERTa embeddingAUROC0.93—Unverified
7ResNet + OpenAI embeddingAUROC0.92—Unverified
8FTTransformer + OpenAI embeddingAUROC0.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Trompt + OpenAI embeddingAUROC0.81—Unverified
2Multimodal-Net All-TextAUROC0.8—Unverified
3ResNet + RoBERTa finetuneAUROC0.79—Unverified
4LightGBM + RoBERTa embeddingAUROC0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Attention based CNNF1 score0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoostF1-Score98.79—Unverified