A practical approach to classify evolving data streams: Training with limited amount of labeled data

Computer Science

• Data Stream 100%
• Classification Models 100%
• Botnets 50%
• Clustering Technique 50%
• Training Data 50%
• Synthetic Data 50%
• Learning Algorithm 50%
• Labeled Instance 50%
• Classification Algorithm 50%
• Unlabeled Data 50%
• Supervised Learning 50%

Keyphrases

• Amount of Training 20%
• Cluster Classification 20%