C4.5: programs for machine learning
C4.5: programs for machine learning
Parallel Formulations of Decision-Tree Classification Algorithms
Data Mining and Knowledge Discovery
Machine Learning
The Effect of Numeric Features on the Scalability of Inductive Learning Programs
ECML '95 Proceedings of the 8th European Conference on Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Grid resource management: state of the art and future trends
Grid resource management: state of the art and future trends
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Decision Tree Construction for Data Mining on Grid Computing Environments
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Parallel univariate decision trees
Pattern Recognition Letters
Overlay Networks with Linear Capacity Constraints
IEEE Transactions on Parallel and Distributed Systems
An optimal discrete rate allocation for overlay video multicasting
Computer Communications
Parallel k-most similar neighbor classifier for mixed data
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Since the amount of information is rapidly growing, there is an overwhelming interest in efficient network computing systems including Grids, public-resource computing systems, P2P systems and cloud computing. In this paper we take a detailed look at the problem of modeling and optimization of network computing systems for parallel decision tree induction methods. Firstly, we present a comprehensive discussion on mentioned induction methods with a special focus on their parallel versions. Next, we propose a generic optimization model of a network computing system that can be used for distributed implementation of parallel decision trees. To illustrate our work we provide results of numerical experiments showing that the distributed approach enables significant improvement of the system throughput.