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Robust Weighted Random Forest with Imbalanced Classification Problems

Abstract Number:

2800 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Sunwoo Han (1), Yunbi Nam (2)

Institutions:

(1) University of Miami, N/A, (2) Vanderbilt University, N/A

Co-Author:

Yunbi Nam  
Vanderbilt University

First Author:

Sunwoo Han  
University of Miami

Presenting Author:

Sunwoo Han  
University of Miami

Abstract Text:

In many applications, it is common to have numerous features with different levels of information and an imbalanced outcome ratio simultaneously. Weighted Random Forest (WRF) has been utilized to address low-signal-to-noise problem by assigning more weights to informative features prioritizing the inclusion of a feature subset at each node of individual trees. However, it has not been actively studied in class imbalanced problem. In this work, we propose to use RF variable importance in the area under the receiver operating characteristic curve - referred to VI-AUC - as weights with WRF to account for class imbalanced problems. Our simulation studies show that WRF with VI-AUC is superior and stable compared to other weighting methods, particularly in class imbalanced scenarios with small sample size. Applications using an immunologic marker dataset from an HIV vaccine efficacy trial are illustrated.

Keywords:

Variable importance|Weighted random forest|Class imbalance|AUC| |

Sponsors:

ENAR

Tracks:

Miscellaneous

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