Journal
APPLIED INTELLIGENCE
Volume 51, Issue 8, Pages 5683-5700Publisher
SPRINGER
DOI: 10.1007/s10489-020-01952-5
Keywords
Evolutionary data mining; Big data; Decision trees; Parallel computing; Graphics processing unit (GPU); CUDA
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Funding
- Bialystok University of Technology - Ministry of Science and Higher Education [WZ/WI-IIT/3/2020]
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This paper discusses the evolutionary induction of decision trees for large-scale data and proposes a novel multi-GPU approach to speed up the process. Experimental validation shows satisfactory acceleration, with the solution capable of processing billions of instances. The impact of data characteristics on the evolutionary search speedup is also demonstrated.
This paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU's resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.
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