Robust Tree-Based Incremental Imputation Method for Data Fusion
published: Oct. 8, 2007, recorded: September 2007, views: 266
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !