Classification Using Ensemble Learning Under Weighted Misclassification Loss

• Activity Number: 39 - Methods in Financial Risk Assessment
• Type: Contributed
• Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Risk Analysis
• Author(s): Yizhen Xu* and Tao Liu and Rami Kantor and Ann Mwangi and Michael J Daniels and Joseph Hogan
• Companies: Brown University and Brown University, Dept of Biostatistics and Brown Univeresity School of Medicine and Moi University and University of Texas at Austin and Brown University, Dept of Biostatistics
• Keywords: Classification ; Ensemble Learner ; Weighted Misclassification Risk

Abstract

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. Individual-level monitoring of HIV-infected individuals on antiviral treatment requires periodic assessment of treatment failure, viral load (VL) above a certain threshold. In resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Higher premium is placed on avoiding false-positives which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification risk.

We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification risk. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score.