TWEEF: Trustworthiness Estimation and Enhancement Framework for Machine Learning Models

Computer Science

• Interpretability 100%
• Machine Learning Model 100%
• Linguistics 100%
• Predictive Performance 50%
• Predictive Model 50%
• Performance Metric 50%
• Model Generation 50%
• Machine Learning 50%
• Learning System 50%
• Fuzzy Mathematics 50%
• Fairness Performance 50%
• Binary Classification 50%
• Bias Mitigation 50%
• Weighted Average 50%
• Unified Pipeline 50%

INIS

• machine learning 100%
• metrics 100%
• performance 75%
• evaluation 50%
• assessments 50%
• aggregation 50%
• tools 25%
• configuration 25%
• ecosystems 25%
• datasets 25%
• dimensions 25%
• learning 25%
• foundations 25%
• benchmarks 25%
• classification 25%
• calculation methods 25%
• adults 25%
• pipelines 25%
• fuzzy logic 25%
• mitigation 25%
• credits 25%

Keyphrases

• Trustworthiness 100%
• Machine Learning Models 100%
• Subjective Logic 25%
• Benchmark Dataset 12%
• Performance Prediction 12%
• Machine Learning 12%
• Performance Evaluation 12%
• Performance Metrics 12%
• Surrogate Model 12%
• Linguistic Assessment 12%
• Learning Performance 12%
• Learning Task 12%
• Binary Classification 12%
• Configuration Scheme 12%
• Weighted Aggregation 12%
• Weighting Scheme 12%
• Model Trust 12%
• Metrics Computation 12%
• Gaussian Weight 12%
• Additional Learning 12%
• Impact Domains 12%
• COMPAS 12%
• Flexible Foundation 12%
• Intuitionistic Fuzzy Logic 12%
• Extensible Framework 12%
• Trust Enhancement 12%
• Linguistic Weighted Average 12%
• Evaluation Dimension 12%
• Scikit-learn 12%
• Bias Mitigation 12%
• Performance Fairness 12%
• Mitigation Procedures 12%
• Model Generation 12%
• Trust Aggregation 12%
• Modular Framework 12%
• Resulting Trusts 12%
• Predictive Model 12%
• Joint Evaluation 12%
• Conventional Performance 12%
• Trust Assessment 12%
• Trust Score 12%