Keyphrases
Augmented Dataset
22%
Binary Classification
22%
Blow Molding
88%
CatBoost Classifier
22%
Characteristic-based
44%
Class Imbalance
100%
Classification Model
22%
Cluster-based
44%
Clustering Methods
22%
Conditional Generative Adversarial Network (cGAN)
44%
Credit Card Fraud Detection
93%
Credit Risk
44%
Credit Risk Prediction
44%
Data Characteristics
44%
Data Synthetization
22%
Decision Tree
22%
Default Prediction
44%
Design Space
35%
Ensemble Learning
22%
Family Loans
22%
Financial Data
27%
Fraud
18%
Freddie Mac
22%
Generative Adversarial Networks
44%
Hierarchical Clustering
22%
Hyperparameters
50%
Inflatable Structures
44%
Loan Default
22%
Loan Default Prediction
44%
Low-cost 3D
44%
Minority Class
27%
Model Bias
22%
Model Performance
22%
Molding Process
35%
Multi-layer Perception
22%
Network Technique
44%
Oversampling Technique
94%
Performance Evaluation Index
22%
Performance Prediction
22%
Preprocessing Techniques
22%
Public Loan
22%
Reinforcement Learning
44%
Simpson's Paradox
22%
Single-family
22%
State-of-the-art Techniques
22%
Synthetic Approaches
44%
Synthetic Minority Oversampling Technique (SMOTE)
88%
Testing Sample
22%
Training Samples
22%
XGBoost Classifier
22%
Computer Science
Applied Technique
6%
Binary Classification
22%
Class Distribution
14%
Class Imbalance
88%
Class Imbalance Problem
20%
Classification Models
22%
Classification Performance
6%
Classification Problem
22%
Convolutional Neural Network
12%
Data Generation
12%
Data Normality
14%
Decision Trees
22%
Detection Rate
14%
Ensemble Learning
22%
Evaluation Metric
51%
Experimental Result
36%
Extreme Gradient Boosting
22%
Feed Forward Neural Networks
6%
Feedforward Neural Network
6%
Financial Data
14%
Forward Neural Network
6%
Fraud Detection
58%
Generalizability
36%
Generative Adversarial Networks
44%
Hierarchical Clustering
22%
Leaning Parameter
44%
Machine Learning
44%
Minority Class
36%
Performance Evaluation
22%
Performance Model
22%
Predictive Performance
22%
Preprocessing Technique
22%
