Model Level Combination of Tree Ensemble Hyperboxes via GFMM

摘要：

An ensemble of decision trees defines an overlapping set of hyperboxes. These hyperboxes in turn define a disjoint set of hyperboxes each with an associated vector of individual decisions. These vectors can be used to robustly label the hyperboxes by class, or to define soft labels. We sample from these hyperboxes and use them to build a single classifier within the General Fuzzy Min-Max (GFMM) framework that gains information from many different resamplings of the data through the ensemble from which it is built. This method is found to build robust GFMM models, with improved performance on most datasets compared to the basic GFMM.

作者: Mark Eastwood Bogdan Gabrys

作者单位: School of Design, Engineering and Computing, Bournemouth University

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 446-450

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）