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Activity Number: 497 - Variable Selection
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313688
Title: Cluster Group Variable Selection Method for High-Dimensional Data
Author(s): Qingcong Yuan* and Zhiyuan Li
Companies: Miami University and Miami University
Keywords: Cluster ; Group variable selection; High dimensional data

We consider variable selection problems with correlated variables structure for high dimensional data. When predictor variables are correlated, many traditional veriable seleciotn methods show weak performance. To increase variable selection accuracy under such situation, group variable selection methods are widely used. However, the group structure among predictors usually is not known, especially for high dimensional data. Thus, we propose a cluster group variable selection method. The method first discovers the group structure considering the relationship among predictor variables, then conduct group variable selection methods. In addition, algorithm to analyze datasets with much larger number of predictors and observations is introduced. We compared the performance of proposed method with different group variable selection methods to show the advantages.

Authors who are presenting talks have a * after their name.

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