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Tuning Parameter
100%
Instance Selection
100%
Bug Resolution
100%
Selection Algorithm
70%
Machine Learning Classifiers
40%
Grid Search
40%
Machine Learning Techniques
30%
Single Instance
30%
Edited Nearest Neighbor
30%
Resolution Prediction
30%
Support Vector Machine
20%
Software Maintenance
20%
K-nearest Neighbor (K-NN)
20%
Parameterized Method
20%
Reduced Data
20%
Performance Prediction
10%
Grid Support
10%
Performance Criteria
10%
10-fold Cross Validation
10%
Prediction Model
10%
Parameter Configuration
10%
Parameter Tuning
10%
Random Tree
10%
Bug Reports
10%
Historical Data
10%
Cross-validation Method
10%
Homogeneous-heterogeneous
10%
Maintenance Effort
10%
APACHE
10%
Recall Performance
10%
Precision Performance
10%
Random Features
10%
Eclipse Platform
10%
Neighborhood Support
10%
Accuracy Performance
10%
Homogeneous Ensemble
10%
LibreOffice
10%
Heterogeneous Ensemble
10%
Optimal Prediction
10%
Voting Ensemble
10%
Software Process Improvement
10%
Feature Subset Ensembles
10%
Precision Accuracy
10%
Software Context
10%
Man-days
10%
Voted Perceptron
10%
Prediction Machines
10%
Proprietary Software
10%
Software Bug Report
10%
Estimating Software
10%
Computer Science
Instance Selection
100%
Machine Learning
40%
Learning System
40%
Open Source Software
30%
Machine Learning Technique
30%
Single Instance
30%
Support Vector Machine
20%
Software Maintenance
20%
bug report
20%
Maintenance Effort
20%
Parameter Method
20%
Performance Criterion
10%
Prediction Performance
10%
Fold Cross Validation
10%
Prediction Model
10%
Historical Data
10%
Validation Method
10%
Basis Algorithm
10%
Eclipse Platform
10%
Proprietary Software
10%
Default Parameter
10%