Decision Tree Induction from Distributed Heterogeneous Autonomous Data Sources

Abstract

With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed, heterogeneous and autonomous data repositories. In many applications, practical constraints require such systems to provide support for data analysis where the data and the computational resources are available. This presents us with distributed learning problems. We precisely formulate a class of distributed learning problems; present a general strategy for transforming traditional machine learning algorithms into distributed learning algorithms; and demonstrate the application of this strategy to devise algorithms for decision tree induction (using a variety of splitting criteria) from distributed data. The resulting algorithms are provably exact in that the decision tree constructed from distributed data is identical to that obtained by the corresponding algorithm when in the batch setting. The distributed decision tree induction algorithms have been implemented as part of INDUS, an agent-based system for data-driven knowledge acquisition from heterogeneous, distributed, autonomous data sources.