Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014 (2014), Article ID 745640, 12 pages
Research Article

CUDT: A CUDA Based Decision Tree Algorithm

1Department of Computer Science, Tung Hai University, Taichung 40704, Taiwan
2Department of Computer Science and Information Engineering, National Taipei University, New Taipei 23741, Taiwan
3Department of Computer Science, National Chiao Tung University, Hsinchu 30010, Taiwan

Received 22 May 2014; Accepted 17 June 2014; Published 22 July 2014

Academic Editor: Jason J. Jung

Copyright © 2014 Win-Tsung Lo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [7 citations]

The following is the list of published articles that have cited the current article.

  • Cheng Li, Wei Wang, Longfei Shi, and Xuesong Wang, “Recognition and Parameter Extraction of One-Dimensional Electronic Scanning for 3D Radar,” International Journal of Antennas and Propagation, vol. 2014, pp. 1–9, 2014. View at Publisher · View at Google Scholar
  • D. Strnad, and A. Nerat, “Parallel construction of classification trees on a GPU,” Concurrency and Computation: Practice and Experience, 2015. View at Publisher · View at Google Scholar
  • Bhanu Prakash Battula, Rama Krishna, and Tai-Hoon Kim, “An efficient approach for knowledge discovery in decision trees using inter quartile range transform,” International Journal of Control and Automation, vol. 8, no. 7, pp. 325–334, 2015. View at Publisher · View at Google Scholar
  • Krzysztof Jurczuk, Marcin Czajkowski, and Marek Kretowski, “Evolutionary induction of a decision tree for large-scale data: a GPU-based approach,” Soft Computing, vol. 21, no. 24, pp. 7363–7379, 2016. View at Publisher · View at Google Scholar
  • Xavier Limón, Alejandro Guerra-Hernández, Nicandro Cruz-Ramírez, Héctor-Gabriel Acosta-Mesa, and Francisco Grimaldo, “A Windowing strategy for Distributed Data Mining optimized through GPUs,” Pattern Recognition Letters, 2016. View at Publisher · View at Google Scholar
  • Rory Mitchell, and Eibe Frank, “Accelerating the XGBoost algorithm using GPU computing,” PeerJ Computer Science, vol. 3, pp. e127, 2017. View at Publisher · View at Google Scholar
  • Akira Jinguji, Shimpei Sato, and Hiroki Nakahara, “An FPGA Realization of a Random Forest with k-Means Clustering Using a High-Level Synthesis Design,” IEICE Transactions on Information and Systems, vol. E101.D, no. 2, pp. 354–362, 2018. View at Publisher · View at Google Scholar