Abstract
The growing availability of large amounts of multimedia contents in science and industry have made data mining applications such as data classification highly demanding. The contribution of this paper is two-fold. First, we propose an approach for constructing a decision tree based classification model for multimedia contents. Second, in order to speed up the performance of the proposed model, we propose a hybrid CPU-GPU approach for construction of decision tree on Graphic Processing Unit (GPU). Our approach not only accelerates the construction of decision tree via GPU computing, but also does so by considering the power and energy consumption of the GPU. Through the experiments, we demonstrate that the proposed hybrid CPU-GPU approach outperforms CPU-based sequential implementation by several times.
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Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012003797).
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Nasridinov, A., Ihm, SY. & Park, YH. A hybrid construction of a decision tree for multimedia contents. Multimed Tools Appl 74, 8455–8465 (2015). https://doi.org/10.1007/s11042-013-1614-6
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DOI: https://doi.org/10.1007/s11042-013-1614-6