Ordinal Risk-Group Classification

TL;DR

This paper introduces a new approach to ordinal risk-group classification, improving how risk predictions are discretized into ordered categories, with theoretical bounds and an augmented logistic regression method demonstrated through a numeric example.

Contribution

It proposes a novel framework with constraints and penalties to enhance risk-group discretization, extending logistic regression for ordinal risk classification.

Findings

Established lower bounds on existing methods' accuracy.

Augmented logistic regression effectively classifies ordinal risk groups.

Demonstrated improved risk discretization with a numeric example.

Abstract

Most classification methods provide either a prediction of class membership or an assessment of class membership probability. In the case of two-group classification the predicted probability can be described as "risk" of belonging to a "special" class . When the required output is a set of ordinal-risk groups, a discretization of the continuous risk prediction is achieved by two common methods: by constructing a set of models that describe the conditional risk function at specific points (quantile regression) or by dividing the output of an "optimal" classification model into adjacent intervals that correspond to the desired risk groups. By defining a new error measure for the distribution of risk onto intervals we are able to identify lower bounds on the accuracy of these methods, showing sub-optimality both in their distribution of risk and in the efficiency of their resulting partition into intervals. By adding a new form of constraint to the existing maximum likelihood optimization framework and by introducing a penalty function to avoid degenerate solutions, we show how existing methods can be augmented to solve the ordinal risk-group classification problem. We implement our method for logistic regression (LR) and show a numeric example.