On Calibration of Nested Dichotomies

摘要：

　　Nested dichotomies(NDs)are used as a method of transforming a multiclass classification problem into a series of binary problems.A tree structure is induced that recursively splits the set of classes into subsets,and a binary classification model learns to discriminate between the two subsets of classes at each node.In this paper,we demonstrate that these NDs typically exhibit poor probability calibration,even when the binary base models are well-calibrated.We also show that this problem is exacerbated when the binary models are poorly calibrated.We discuss the effectiveness of different calibration strategies and show that accuracy and log-loss can be significantly improved by calibrating both the internal base models and the full ND structure,especially when the number of classes is high.

作者: Tim Leathart Eibe Frank Bernhard Pfahringer Geoffrey Holmes

作者单位: Department of Computer Science,University of Waikato,Hamilton,New Zealand

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 69-80

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）