Extensions to Online Feature Selection Using Bagging and Boosting

Keyphrases

• Online Feature Selection 100%
• Feature Subset Selection 66%
• L0 Norm 33%
• CPU Availability 33%
• Feature Information 33%
• Parameter Vector 33%
• Learning Problems 33%
• Ensemble Model 33%
• Informative Subsets 33%
• Base Classifier 33%
• Sparse Linear Models 33%
• Art Features 33%
• Partial Characteristics 33%
• Large Volumes of Data 33%
• Resource Constraints 33%

Computer Science

• Feature Subset Selection 50%
• Sparsity 25%
• Resource Constraint 25%
• Parameter Vector 25%
• Base Classifier 25%
• Selection Method 25%
• Feature Information 25%