Generation of controlled synthetic samples and impact of hyper-tuning parameters to effectively classify the complex structure of overlapping region

Keyphrases

• Ensemble Classifier 100%
• Complex Structure 100%
• Overlapping Region 100%
• Hypertuning 100%
• Tuning Parameter 100%
• Synthetic Samples 100%
• Imbalanced Data 75%
• Imbalanced Classification 75%
• Machine Learning Classifiers 50%
• Imbalanced Distribution 50%
• Majority Class 50%
• Multi-class Problem 50%
• Hybrid Method 25%
• Learning Improvement 25%
• HyPer 25%
• Problem Domain 25%
• Learning Behavior 25%
• Sampling Methods 25%
• Real-world Application 25%
• Traditional Classifiers 25%
• Machine Learning Algorithms 25%
• Ensemble Methods 25%
• Public Dataset 25%
• First Kind 25%
• Multiple Classes 25%
• Learning from Imbalanced Data 25%
• Balanced Dataset 25%
• Class Overlap 25%
• Hybrid Ensemble 25%
• Minority Class 25%
• Transformation Method 25%
• Classification Boundary 25%
• Multi-class Data 25%
• Algorithm Adaptation 25%
• Grid Search 25%
• Overlapping Data 25%
• Adaptation Methods 25%

Computer Science

• ensemble classifier 100%
• Complex Structure 100%
• Overlapping Region 100%
• Imbalanced Data 75%
• Majority Class 50%
• Multiclass Problem 50%
• Machine Learning 50%
• Learning System 50%
• Problem Domain 25%
• World Application 25%
• Machine Learning Algorithm 25%
• Sampling Technique 25%
• Ensemble Technique 25%
• Search Technique 25%
• Classification Problem 25%
• Sample Dataset 25%
• Minority Class 25%
• Adaptation Algorithm 25%