# Classification using Ensemble Learning under Weighted Misclassification Loss

## TL;DR

This paper introduces a method for optimizing binary classification rules under weighted misclassification loss using ensemble learning, improving accuracy and operating characteristics in resource-limited health monitoring scenarios.

## Contribution

It develops a joint score and threshold estimation method for weighted misclassification, enhancing classification performance over existing sequential approaches.

## Key findings

- More accurate risk estimation in simulations
- Better operating characteristics in finite samples
- Effective in resource-limited health monitoring

## 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. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an 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 especially for finite samples.