图 书 馆
基于鲁棒树的数据融合增量填充方法Robust Tree-Based Incremental Imputation Method for Data Fusion |
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| 课程网址: | http://videolectures.net/ida07_dambrosio_rtbi/ |
| 主讲教师: | Antonio D Ambrosio |
| 开课单位: | 那不勒斯大学 |
| 开课时间: | 2007-10-08 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Data Fusion and Data Grafting are concerned with combining files and information coming from different sources. The problem is not to extract data from a single database, but to merge information collected from different sample surveys. The typical data fusion situation formed of two data samples, the former made up of a complete data matrix X relative to a first survey, and the latter Y which contains a certain number of missing variables. The aim is to complete the matrix Y beginning from the knowledge acquired from the X. Thus, the goal is the definition of the correlation structure which joins the two data matrices to be merged. In this paper, we provide an innovative methodology for Data Fusion based on an incremental imputation algorithm in tree-based models. In addition, we consider robust tree validation by boosting iterations. A relevant advantage of the proposed method is that it works for a mixed data structure including both numerical and categorical variables. As benchmarking methods we consider explicit methods such as standard trees and multiple regression as well as an implicit method based principal component analysis. A widely extended simulation study proves that the proposed method is more accurate than the other methods. |
| 关 键 词: | 计算机科学; 机器学习; 预处理 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-09-28：heyf |
| 阅读次数: | 56 |