The Univariate Flagging Algorithm (UFA): a Fully-Automated Approach for Identifying Optimal Thresholds in Data

TL;DR

The paper introduces the Univariate Flagging Algorithm (UFA), an automated method for detecting optimal thresholds in data that improve classification, especially in cases with non-linear relationships and low target incidence.

Contribution

The paper presents UFA, a novel automated threshold detection algorithm that outperforms traditional classifiers and is robust, scalable, and easy to interpret.

Findings

UFA identifies thresholds that align with expert judgment.

UFA achieves equal or better performance than traditional classifiers.

UFA is robust to missing data and noise.

Abstract

In many data classification problems, there is no linear relationship between an explanatory and the dependent variables. Instead, there may be ranges of the input variable for which the observed outcome is signficantly more or less likely. This paper describes an algorithm for automatic detection of such thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm searches for a separation that optimizes the difference between separated areas while providing the maximum support. We evaluate its performance using three examples and demonstrate that thresholds identified by the algorithm align well with visual inspection and subject matter expertise. We also introduce two classification approaches that use UFA and show that the performance attained on unseen test data is equal to or better than that of more traditional classifiers. We demonstrate that the proposed algorithm is robust against missing data and noise, is scalable, and is easy to interpret and visualize. It is also well suited for problems where incidence of the target is low.