Local Rotation Forest of Decision Stumps for Regression Problems

摘要：

Parametric models such as linear regression can contribute valuable, interpretable descriptions of simple structure in data. However, occasionally such simple structure does not extend across an entire database and might be confined more locally within subsets of the data. Nonparametric regression normally involves local averaging. In this study, local averaging estimator is coupled with a machine learning technique -Rotation Forest. In more detail, we propose a technique of local rotation forest of decision stumps. We performed a comparison with other well known methods and ensembles, on standard benchmark datasets and the performance of the proposed technique was greater in most cases.

关键词： machine learning data mining regression

作者: S. B. Kotsiantis P. E. Pintelas

作者单位: Educational Software Development Laboratory Department of Mathematics Greece

会议类型: 国际会议

会议名称: 2009 2nd IEEE International Conference on Computer Science and Information Technology(第二届计算机科学与信息技术国际会议 ICCSIT2009)

会议地点: 北京

会议语种:英文

页码: 2927-2931

在线出版日期: 2009-08-08（万方平台首次上网日期，不代表论文的发表时间）