Abstract
Decision trees are well known, widely used algorithm for building efficient classifiers.We propose the modification of the Parallel Hoeffding Tree algorithm that could deal with large streaming data. The proposed method were evaluated on the basis of computer experiment which were carried on few real datasets. The algorithm uses parallel approach and the Hoeffding inequality for better performance with large streaming data. The paper present the analysis of Hoeffding tree and its issues.
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Cal, P., Woźniak, M. (2013). Parallel Hoeffding Decision Tree for Streaming Data. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_4
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DOI: https://doi.org/10.1007/978-3-319-00551-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00550-8
Online ISBN: 978-3-319-00551-5
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