计算机科学 ›› 2017, Vol. 44 ›› Issue (1): 48-52.doi: 10.11896/j.issn.1002-137X.2017.01.009
吴梅红,郭佳盛,鞠颖,林子雨,邹权
WU Mei-hong, GUO Jia-sheng, JU Ying, LIN Zi-yu and ZOU Quan
摘要： 提出一种选择性集成学习算法,该算法利用多线程并行优化基分类器的参数,通过多层筛选和动态更新筛选信息获取最优的候选基分类器集合,解决了以往在集成学习中选择分类器效率低下的问题。集成分类器采用分解合并的策略进行加权投票,通过使用二分法将大数据集的投票任务递归分解成多个子任务,并行运行子任务后合并投票结果以缩短集成分类器的投票运行时间。实验结果表明, 相对于传统方法, 所提出的算法在平均精度、F1-Measure以及AUC指标上都有着显著提升。
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