• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>PRICAI'98 : Topics in artificial intelligence >Generating Classifier Committees by Stochastically Selecting both Attributes and Training Examples
【24h】

Generating Classifier Committees by Stochastically Selecting both Attributes and Training Examples

机译：通过随机选择属性和训练示例来生成分类委员会

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Boosting and Bagging, as two representative approaches to learning classifier committees, have demonstrated great success, especially for decision tree learning. They repeatedly build different classifiers using a base learning algorithm by changing the distribution of the training set. Sasc, as a different type of committee learning method, can also significantly reduce the error rate of decision trees. It generates classifier committees by stochastically modifying the set of attributes but keeping the distribution of the training set unchanged. It has been shown that Bagging and Sasc are, on average, less accurate than Boosting, but the performance of the former is more stable than that of the latter in terms of less frequently obtaining significantly higher error rates than the base learning algorithm. In this paper, we propose a novel committee learning algorithm, called SascBag, that combines Sasc and Bagging. It creates different classifiers by stochastically varying both the attribute set and the distribution of the training set. Experimental results in a representative collection of natural domains show that, for decision tree learning, the new algorithm is, on average, more accurate than Boosting, Bagging, and Sasc. It is more stable than Boosting. In addition, like Bagging and Sasc, SascBag is amenable to parallel and distributed processing while Boosting is not. This gives SascBag another advantage over Boosting for parallel machine learning and datamining.
机译：Boosting和Bagging作为学习分类器委员会的两种代表性方法，已经取得了巨大的成功，尤其是在决策树学习方面。他们通过更改训练集的分布，使用基础学习算法反复构建不同的分类器。 Sasc作为一种不同类型的委员会学习方法，也可以显着降低决策树的错误率。它通过随机修改属性集但保持训练集的分布不变来生成分类委员会。已经显示出Bagging和Sasc平均而言不如Boosting准确，但是前者的性能比后者的性能更稳定，这是因为与基学习算法相比，获得频繁更高的错误率的可能性更低。在本文中，我们提出了一种新颖的委员会学习算法，称为SascBag，它结合了Sasc和Bagging。它通过随机地改变属性集和训练集的分布来创建不同的分类器。有代表性的自然域集合中的实验结果表明，对于决策树学习而言，新算法平均比Boosting，Bagging和Sasc更准确。它比Boosting更稳定。此外，像Bagging和Sasc一样，SascBag可以并行和分布式处理，而Boosting则不能。这使得SascBag在并行机器学习和数据挖掘方面比Boosting更具优势。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号