扩大规则归纳的无沟通策略

Violeta N. Ivanova-Rohling
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引用次数: 2

摘要

在大数据时代,随着并行计算资源的不断增加,为了提高现有算法的效率,对数据挖掘并行算法的研究成为人们关注的焦点。我们采取了不同的观点,而不是通常关注算法的加速,我们关注的是投入并行计算资源来提高现有启发式获得的模型的准确性,而不会增加总体运行时间。我们寻求一种策略,使并行计算资源以一种智能的方式投入,以提高搜索空间的探索,而不需要并行工作人员之间的通信。我们在规则归纳算法CN2上证明了它们的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Communication-less Strategies for the Widening of Rule Induction
In the age of Big Data and with the ever increasing availability of parallel compute resources there has been strong focus on research in parallel algorithms for data mining aiming to improve the efficiency of existing algorithms. We take a different view, instead of the usual focus on speed-up of the algorithm, we focus on investing parallel compute resources to improve the accuracy of models obtained by existing heuristics, without increasing the overall running time. We look for strategies to invest parallel compute resources in a smart way in order to improve the search space exploration, without the necessity of communication between the parallel workers. We demonstrate their effectiveness on the rule induction algorithm CN2.
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