Abstract
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.
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Walkowiak, K., Woźniak, M. (2009). Modeling of Network Computing Systems for Decision Tree Induction Tasks. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_93
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DOI: https://doi.org/10.1007/978-3-642-04394-9_93
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