Abstract
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.
Highlights
IntroductionThe term Big Data has become extremely popular in business, industry, and science [14]
In the last decade, the term Big Data has become extremely popular in business, industry, and science [14]
We have focused on graphics processing units (GPUs) to provide parallelization of evolutionary decision trees (DT) induction
Summary
The term Big Data has become extremely popular in business, industry, and science [14] It refers to storing and handling of large or complex datasets that are almost impossible to manage using traditional tools. Recent involvement of evolutionary algorithms (EAs) into the trees induction [2] can be seen as a breath of fresh air. Their main advantage is the global approach in which a tree structure, tests in internal nodes and predictions in leaves are searched simultaneously [38]. The generated trees are significantly simpler and at least as accurate as the greedy alternatives
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