Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses

Keyphrases

• Accurate Estimation 25%
• Actual Conditions 25%
• Balanced Data 25%
• Best Model 25%
• Built Environment 25%
• Classification Model 25%
• Classification Task 25%
• Comparative Analysis 100%
• Computationally Efficient 25%
• Conditional Generative Adversarial Network (cGAN) 25%
• Decision Tree-based Models 25%
• Execution Time 50%
• F1 Score 25%
• Faucet 25%
• Grid Search 25%
• High Performance 25%
• Imbalanced Data 25%
• Labeled Data 50%
• Logistic Regression 25%
• Metering System 25%
• Model Performance 25%
• Neural Networks NN 25%
• NN Model 50%
• Number of Events 25%
• One-class Support Vector Machine (OCSVM) 25%
• Optimized Hyperparameters 25%
• Random Forest 25%
• Residential Water 100%
• RF Model 50%
• Smart Water Metering 25%
• Supervised Classification Algorithm 100%
• Supervised Machine Learning Classifiers 25%
• Sustainable Water Management 25%
• Synthetic Data 25%
• Toilet 25%
• Water End Use 100%

Computer Science

• Classification Algorithm 100%
• Classification Models 33%
• Classification Task 33%
• Comparative Analysis 100%
• Decision Tree 33%
• Execution Time 66%
• Generative Adversarial Networks 33%
• Imbalanced Data 33%
• Labeled Data Set 33%
• Leaning Parameter 33%
• Learning System 33%
• Logistic Regression 33%
• Machine Learning 33%
• Neural Network 33%
• Performance Model 33%
• Random Decision Forest 33%
• Supervised Classification 100%
• Support Vector Machine 100%
• Synthetic Data 33%