江汉大学学报(自然科学版) ›› 2017, Vol. 45 ›› Issue (3): 262-267.doi: 10.16389/j.cnki.cn42-1737/n.2017.03.014

• 计算机与信息科学 • 上一篇    下一篇

三类分类器性能评估方法B-ROCCH 研究

邹洪侠,陶硕   

  1. 马鞍山职业技术学院,安徽 马鞍山 243000
  • 出版日期:2017-06-28 发布日期:2017-06-30
  • 作者简介:邹洪侠(1982—),女,讲师,硕士,研究方向:人工智能、数据挖掘。

Research on Three-Classifier Evaluation Method B-ROCCH

ZOU Hongxia,TAO Shuo   

  1. Ma′anshan Technical College,Ma′anshan 243000,Anhui,China
  • Online:2017-06-28 Published:2017-06-30

摘要: ROCCH 理论主要用于解决代价敏感的二类分类器性能评估问题,如何有效地将其扩展到多类评估中是研究难点。采用二叉树思想和垂直平均方法,提出了一种新的代价敏感的多类分类器性能评估方法BROCCH。B-ROCCH 方法利用二叉树思想将三类分类问题转化为二类分类问题,使用垂直平均方法绘制三类ROC曲线,结合ROCCH思想,判断三类分类问题中的潜在最优分类器和最优分类器。在MBNC平台上对该方法进行了实现,与B-AUC 方法的实验数据进行比较分析,证明B-ROCCH 方法是可行的,且更具可区分性,速度也更快。

关键词: 分类器评估, 代价敏感, 二叉树, 三类分类器

Abstract: ROCCH theory is mainly used to solve the performance evaluation of two-classifier which are cost sensitive,how to effectively extend it to multi class evaluation is the difficulty for research. With binary-tree and vertical average method,a new cost-sensitive three-classifier evaluation method B-ROCCH was made up. B-ROCCH method was used to transform three-classification problems into two-classification problems with the idea of binary-tree,three types of ROC curves were plotted with the vertical average method,then combinated with the ROCCH thoughts,to determine the potential best classifier and the best classifier in the three-classification problems. The method was implemented on the MBNC platform,the experimental data were compared with the B-AUC evaluation method,the results prove that the B-ROCCH method is feasible,more distinguishable and faster.

Key words: classifier evaluation, cost - sensitive, binary-tree, three-classifier

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