• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Fuzzy Systems and Knowledge Discovery >The study of methods for post-pruning decision trees based on comprehensive evaluation standard
【24h】

The study of methods for post-pruning decision trees based on comprehensive evaluation standard

机译：基于综合评价标准的后修剪决策树方法研究

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Post-pruning is a common method of decision tree pruning. However, various post-pruning tends to use a single measure as an evaluation standard of pruning effects. The single and exclusive index evaluation standard of decision tree is subjective and partial, and the decisions after pruning often have a bias. This paper proposes a decision tree post-pruning algorithm based on comprehensive considering various evaluation standards. At the same time considering the classification ability, stability and size, so as to reflect the integrity advantage of the decision tree. The user can choose each standard component weight value according to actual demand, to get a decision tree which has a tendency to meet the actual demand. The experimental results show that the post-pruning algorithm considering the classification accuracy, stability and the size of decision tree, in classification accuracy unchanged or fall under the premise of tiny range, makes a decision tree has a more balanced classification performance and less model complexity.
机译：后修剪是决策树修剪的一种常用方法。但是，各种修剪后的趋势都倾向于使用一种方法作为修剪效果的评估标准。决策树的单一和排他性指标评估标准是主观的和局部的，修剪后的决策往往会有偏差。提出了一种综合考虑各种评价标准的决策树后修剪算法。同时考虑分类能力，稳定性和大小，从而体现出决策树的完整性优势。用户可以根据实际需求选择每个标准成分重量值，得到具有满足实际需求趋势的决策树。实验结果表明，考虑分类精度，稳定性和决策树大小的后修剪算法，在分类精度不变或落入微小范围的前提下，使得决策树具有较均衡的分类性能和较小的模型复杂度。

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