基于MapReduce的树增强型贝叶斯算法的并行实现

发布时间：2018-05-30 20:41

  本文选题：MapReduce + 树增强型贝叶斯算法　； 参考：《激光杂志》2015年12期

【摘要】：为了解决大数据环境下数据日益增大且响应时间要求变短,以及串行贝叶斯分类器效率低且应用复杂度高的问题,提出了基于MapReduce的并行树增强型贝叶斯算法。本算法使用了弱化了独立性的树增强型贝叶斯算法以获得更高的分类精度,同时为了降低响应时间,引入了MapReduce模型,将本算法由串行转为并行,从而提高处理的速度。实验结果表明该算法比传统的树增强型贝叶斯算法具有更高的算法效率且随着数据节点的增加,加速比也同步增加。
[Abstract]:In order to solve the problems of increasing data and shorter response time in big data environment, and low efficiency and high application complexity of serial Bayesian classifier, a parallel tree enhanced Bayesian algorithm based on MapReduce is proposed. In order to reduce the response time, the MapReduce model is introduced to transform the algorithm from serial to parallel, so as to improve the processing speed. Experimental results show that the proposed algorithm is more efficient than the traditional tree enhanced Bayesian algorithm and the speedup ratio increases synchronously with the increase of data nodes.
【作者单位】： 贵阳学院数学与信息科学学院;南京财经大学管理科学与工程学院;
【基金】：贵州省科技厅联合基金(LKG[2013]43号)
【分类号】：TP18;TP338.6
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本文编号：1956750